Posts Tagged ‘Flow cytometry’

Photonics instruments

Larry H. Bernstein, MD, FCAP, Curator



Using Photonic Components to Design and Build Life Science and Analytical Instruments  

Leading analytical and clinical diagnostics instrumentation OEMs rely on integrated light emission and detection solutions to unlock the mysteries around disease and treatment. Photonic solutions encompassing optical, illumination, sensing and optomechanical technologies provide these OEMs with not only convenience and simplicity, but also an accelerated path to market for the development of highly complex life science and analytical instruments. Even standard, off-the-shelf components, when coupled with a modular and expandable architecture design, can be used to create a fully functional flow cytometer that achieves levels of throughput and cell analysis sensitivity comparable to commercial products.

A good example of an analytical instrument is the flow cytometer, which will be used in this article to highlight the development of the optical heart of the analytical instrument system. Flow cytometers are instruments that measure features of cells in a liquid suspension, characterizing and analyzing cell populations using light scatter and fluorescence parameters. These life science instruments began as research laboratory instruments, and they still play an important role in research into immunology and cell biology. However, they are now also used in the clinical laboratory for diagnosis and monitoring of diseases such as HIV/AIDS and blood cancers. The next phase would be for them to become diagnostic tools used in the clinic or doctor’s office lab, right at the point-of-care.


Laser-induced fluorescence in the heart of the flow cytometer.

Laser-induced fluorescence in the heart of the flow cytometer. The laser beam is the violet beam, entering from left, inducing fluorescence in the target flow in the center of the flow channel, entering from top.


Life science and analytical instruments based on optical techniques have moved from university and corporate research laboratories, through central diagnostic and pathology laboratories, to local hospital laboratories. They are now becoming more and more commonplace as diagnostic tools at or close to the point-of-care delivery. Enabling this development is the evolution of optical components and subsystems used within these instruments for the emission, manipulation and detection of light. The result has been increased capability and usability, along with decreased size and cost. For any optical instrument, the beam path can be summarized as follows in Figure 1.

Block diagram of the optical beam path in a typical analytical instrument.

Figure 1. Block diagram of the optical beam path in a typical analytical instrument. Image courtesy of Excelitas Technologies Corp.


Movement toward smaller laser sources

Traditionally, the instrument manufacturer would have had to begin by mastering the photochemical reactions key to the instrument function. The manufacturer would also need to design the complete optical system within the instrument and then manage the sourcing of the necessary components from a large number of other manufacturers. The lasers originally used in the instrument would have been large, powerful argon or krypton ion lasers, needing water cooling and three-phase high-voltage electrical supplies. The beam would have been delivered through a free-space optical system of lenses, filters and microscope objectives to shape the beam and deliver it to the flow cell. Collecting the fluorescent or scattered signal would also have been done using free-space components, including lenses and mirrors, bandpass filters, and complex optomechanical mounts for alignment, allowing the signal to be delivered to the detectors. These detectors would typically have been a photomultiplier tube, a fragile and complex structure of electrodes sealed in glass vacuum tube, requiring over 1000 V to convert the photons entering the detector into a useful electronic output.


Qioptiq iRIS fiber-coupled laser module from Excelitas Technologies Corp.

Qioptiq iRIS fiber-coupled laser module from Excelitas Technologies Corp. Photo courtesy of Excelitas Technologies Corp.

Today’s instrument designer can now use significantly smaller laser sources, using diode lasers and diode-pumped, solid-state lasers. These lasers can generate the wavelengths used to excite the fluorescence and provide the illumination for the scattering measurements. Usually, each excitation laser source is dedicated to one probe on the sample per run. Running parallel processes by using multiple illumination wavelengths fired in series on the sample can speed up the analysis.

Challenges in flow cytometry

In flow cytometry the primary challenge is to focus the light onto a moving sample in the flow stream, which is usually less than 100 microns wide. To gather meaningful data from a moving target, both the detector and illumination source need to be as stable and stationary as possible; otherwise movement from either one will cause image jitter and reduce resolution. The second physical challenge is positioning the beams from multiple light sources in sufficient proximity to generate parallel illumination spots focused in the flow cell, while sufficiently separating these spots to prevent cross-talk in the detection channels, which may collect emitted and fluorescent light simultaneously.


LynX silicon photomultiplier modules, as used in the flow cytometer demo system.

LynX silicon photomultiplier modules, as used in the flow cytometer demo system. Photo courtesy of Excelitas Technologies Corp.


Flow cytometers have traditionally used an optical system of lenses, filters and microscope objectives to shape the beam and deliver it to the flow cell. The principles are well-understood, although this can often result in a long optical beam train, making it more susceptible to the effects of laser jitter. An open beam path with optics mounted at various locations is also more susceptible to thermal temperature differences that will cause beam movement on the sample. Careful alignment of the optics is necessary as small movements can cause large changes in the beam pointing on the sample. As a result, instruments that use free-space optics can be susceptible to physical knocks and changes in environmental conditions, including heat generated by the laser itself.

The single-mode optical fiber solution

One way to guarantee a stable high-quality beam delivered to the sample without the use of complex optical components is to use single-mode optical fiber. This offers several advantages in functionality, from increased laser stability and image resolution to reduced instrument size and greater ruggedness.

LynX silicon photomultiplier modules, as used in the flow cytometer demo system.

Photo courtesy of Excelitas Technologies Corp.

Coupled directly from the laser to the fiber, the beam is much less sensitive to movement and thermal temperature changes, which reduces the need for instrument alignment service visits and creates a more robust instrument design. The fiber also acts as a spatial filter and eliminates any beam astigmatism to create a near-perfect Gaussian profile. The resulting beam is much more stable over time than those in free-space systems, and it is without accumulated errors from multiple optical interfaces. That helps ensure a more reliable and more stable measurement. Additionally, the use of fiber allows the laser source, which generates heat, to be located away from the flow cell. This means more flexibility for instrument layout, including the option of mounting the laser externally to the instrument head. It also simplifies servicing. That is because the optical alignment of the instrument is fixed and is independent of the laser — so there is no need to perform a lengthy realignment process when the laser is serviced.

Collecting the fluorescent and scattering signals from the sample requires the light to be gathered and collimated prior to using filters to select the wavelength area of interest. Again, this can be done completely in free space, or with the use of fiber, to simplify the optical set-up and to provide a stable delivery route to the detectors.

The working flow cytometer as shown at several trade shows and conferences worldwide.

Photo courtesy of Excelitas Technologies Corp.


For detection of these signals, today’s designers can still use photomultiplier tubes (PMTs) for the ultimate in sensitivity. However, the advent of large area avalanche photodiodes (APDs) and silicon photomultipliers (SiPMs), allows a single detector type to support a wider range of scattered and fluorescent wavelengths. The result is a greater number of dyes can be used. An SiPM is a monolithic array of silicon micro-APDs offering high photon detection efficiencies from the near UV through to red and NIR wavelengths, coupled with relatively low dark counts. With low operating voltages compared to PMTs, as well as greater ruggedness and lower cost due to the solid-state nature of a silicon detector, many flow cytometer vendors are switching away from PMTs as their detection solution.

Simplifying the photonic engine design process

Many new and innovative analytical and diagnostic companies are using optical technologies such as flow cytometry to provide solutions to their customers. The goal of modern optical component suppliers is to make the manufacturer’s life easier by shouldering the burden of the complete photonic engine design, allowing the manufacturer to focus on its core competencies. To that end, demonstrating the ease and expediency of developing life science and analytical instruments from the ground up, Excelitas Technologies and its subsidiary Qioptiq decided to build a fully functional modular flow cytometry prototype. This prototype uses off-the-shelf products from Excelitas’ various divisions.

The design process began with lasers as excitation sources, incorporating two compact solid-state Qioptiq iFLEX-iRIS lasers, with one being blue and the other being violet. The violet laser took advantage of novel violet-excitable fluorescent dyes recently introduced for flow cytometry. The units were coupled with fiber optic delivery systems, allowing them to be positioned some distance from the sample for accessibility and an efficient design. They also simplified the alignment process and made for plug-and-play laser head replacements.

To capture and collimate the scattered and fluorescent light, two Optem High Resolution 20× microscope objectives were used. One was used for forward-scattered light (indicative of cell size) and the other was used for side-scattered light and fluorescence. Sets of longpass and bandpass filters ensured transmission of the appropriate light wavelengths to the detectors. Six high-performance SiPM modules were also used for photon detection, with one being used for the forward-scattered light, and a second for the side-scattered light. The other four detected the spectral emission wavelengths for fluorescence intensity, based on the lasers selected. The modules were SiPMs integrated with a power supply and amplifier, and they were simpler to integrate than conventional photomultiplier tubes or silicon components.

LINOS Microbench parts were used for organizing the optical bench. Anodized aluminum cubes with stainless steel posts, along with mounting plates, adaptors and connectors, were used with a compatible stainless steel rail system to secure small optical components and create the system layout. Microbench parts assemble and disassemble easily, yet they can be stably aligned, making impromptu adjustments to the layout practical. Cubes could be placed next to one another or stacked, simplifying the layout of components on all three axes. The system was also used to mount the fluidics and flow cell needed for a fully functional flow cytometer.

The system alignment was fine-tuned after assembly using fluorescence dyes and calibration particles. To show that the instrument met specifications, standard fluorescence reference particles were run on the completed system. This successful project clearly demonstrated the power of using standard, off-the-shelf components and of a design approach based on a modular and expandable architecture. This fully functional flow cytometer, designed and built from scratch in less than two months, achieved levels of throughput and cell analysis sensitivity comparable to commercial products.

From modularity and flexibility follows customization for reduced size and cost. That is what manufacturers must strive to achieve when striving to provide instruments — and not only cytometers — to users worldwide.

Meet the author

Richard Simons is the senior applications specialist with Excelitas Technologies Corp. in Montreal; e-mail: richard.simons@excelitas.com

see also –




Fluorescence imaging method visualizes nine structures in one measurement

03/23/2016       Associate Editor, BioOptics World


A research collaboration involving the University of Würzburg (Würzburg, Germany), the University of Göttingen (Göttingen, Germany), and PicoQuant (Berlin, Germany) has discovered a novel strategy for fluorescence lifetime imaging (FLIM) that allows visualizing nine different target molecules at the same time.

Related: Fluorescence ‘lifetime’ moves toward clinical application

As a fundamental imaging technique in life sciences, FLIM makes it possible to visualize structures and processes in cells by exciting molecules with light and employing the lifetime information of the triggered fluorescence. The newly devised approach, named spectrally resolved FLIM (sFLIM), uses three lasers with different wavelengths pulsing in an alternating pattern for exciting the molecular labels. As these labels exhibit subtle differences in their emission spectra and fluorescence decay patterns, the collected data can be analyzed through a software algorithm, allowing distinction between them with unparalleled precision.

Part of a cell labeled for tubulin with Abberior STAR 635p (green) and giantin with Atto647N (red) utilizing a single stimulated emission depletion (STED) laser wavelength, where the two species have been separated by fast pattern matching. (Sample courtesy of Markus Sauer, University of Würzburg, Germany)

The high sensitivity of sFLIM also allows using the same fluorescent dye to label three different cell structures at once, with the ability to still clearly distinguish them because of slight fluorescence lifetime variations induced by the chemical environment.

Multi-target spectrally resolved fluorescence lifetime imaging microscopy

Thomas NiehörsterAnna LöschbergerIngo GregorBenedikt KrämerHans-Jürgen RahnMatthias PattingFelix KoberlingJörg Enderlein & Markus Sauer
Nature Methods13,257–262(2016) 


We introduce a pattern-matching technique for efficient identification of fluorophore ratios in complex multidimensional fluorescence signals using reference fluorescence decay and spectral signature patterns of individual fluorescent probes. Alternating pulsed laser excitation at three different wavelengths and time-resolved detection on 32 spectrally separated detection channels ensures efficient excitation of fluorophores and a maximum gain of fluorescence information. Using spectrally resolved fluorescence lifetime imaging microscopy (sFLIM), we were able to visualize up to nine different target molecules simultaneously in mouse C2C12 cells. By exploiting the sensitivity of fluorescence emission spectra and the lifetime of organic fluorophores on environmental factors, we carried out fluorescence imaging of three different target molecules in human U2OS cells with the same fluorophore. Our results demonstrate that sFLIM can be used for super-resolution multi-target imaging by stimulated emission depletion (STED).


Multi-target fluorescence imaging of five similar fluorescent probes.

(a) Total fluorescence intensity image of an U2OS cell labeled with five fluorescent probes. (bg) Resulting sFLIM composite image (b) showing F-actin stained with ATTO 488 phalloidin (green; c), Golgi stained with primary rabbit antibo…


IR spectroscopy method is promising for clinical test that could detect Alzheimer’s early

Recognizing that current Alzheimer’s disease diagnosis methods only address symptomatic treatment, researchers at Ruhr University Bochum (RUB; Germany) and collaborators at the University of Göttingen and the German Center for Neurogenerative Diseases (DZNE; Göttingen, Germany) have developed a clinical test based on immuno-chemical analysis using infrared (IR)-induced difference spectroscopy that may lead to early detection of Alzheimer’s.

Related: Laser-based eye test for early detection of Alzheimer’s

The promising test involves an IR sensor, whose surface is coated with highly specific antibodies that extract biomarkers for Alzheimer’s from the blood or the cerebrospinal fluid taken from the lower part of the back (lumbar liquor). The IR sensor then performs spectroscopic analysis to determine if the biomarkers show already pathological changes, which can take place more than 15 years before any clinical symptoms appear.

“If we wish to have a drug at our disposal that can significantly inhibit the progress of the disease, we need blood tests that detect Alzheimer’s in its pre-dementia stages,” says Prof. Dr. Klaus Gerwert, Head of the Department of Biophysics at RUB. “By applying such drugs at an early stage, we could prevent dementia, or at the very least delay its onset,” adds Prof. Dr. med. Jens Wiltfang, Head of the Department for Psychiatry and Psychotherapy at the University of Göttingen and Clinical Research Coordinator at DZNE Göttingen.

For the test, the secondary structure of beta amyloid (Aß) peptides serves as a biomarker. This structure changes in Alzheimer’s patients. In the misfolded, pathological structure, more and more Aß peptides can accumulate, gradually forming visible plaque deposits in the brain that are typical for Alzheimer’s disease. This, as mentioned previously, happens more than 15 years before first clinical symptoms are present. The pathological Aß plaques can be temporarily detected by positron emission tomography (PET), but this procedure is comparatively expensive and is accompanied by radiation exposure.

The IR sensor developed by the research team that detects misfolding of Aß peptides involves extracting the Aß peptide from body fluids. After initially working with cerebrospinal fluid, the researchers subsequently expanded the method towards blood analysis. “We do not merely select one single possible folding arrangement of the peptide; rather, we detect how all existing Aß secondary structures are distributed, in their healthy and in their pathological forms,” says Gerwert. Precise diagnostics is not  possible until the distribution of all secondary structures is evaluated. Tests that analyze Aß peptide are already available with enzyme-linked immunosorbent assays (ELISA). They identify the total concentration, percentage of forms of different length, as well as the concentration of individual conformations in body fluids, but they do not provide information on the diagnostically relevant distribution of the secondary structures at once. “This is why ELISA tests have not been proven very effective when applied in blood sample analysis in practice,” he explains.

The research team used the new method to analyze samples from 141 patients, achieving diagnostic precision of 84% in the blood and 90% in cerebrospinal fluid. The test revealed an increase of misfolded biomarkers as spectral shift of Aß band below threshold, allowing the researchers to determine Alzheimer’s.

An infrared sensor analysing label-free the secondary structure of the Abeta peptide in presence of complex fluids

Andreas Nabers1,†, et al.   Journal of Biophotonics Volume 9, Issue 3, pages 224–234, March 2016     http://dx.doi.org:/10.1002/jbio.201400145

Thumbnail image of graphical abstract

An immunologic ATR-FTIR sensor for Abeta peptide secondary structure analysis in complex fluids is presented.

The secondary structure change of the Abeta peptide to beta-sheet was proposed as an early event in Alzheimer’s disease. The transition may be used for diagnostics of this disease in an early state. We present an Attenuated Total Reflection (ATR) sensor modified with a specific antibody to extract minute amounts of Abeta peptide out of a complex fluid. Thereby, the Abeta peptide secondary structure was determined in its physiological aqueous environment by FTIR-difference-spectroscopy. The presented results open the door for label-free Alzheimer diagnostics in cerebrospinal fluid or blood. It can be extended to further neurodegenerative diseases.





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Hematological Malignancy Diagnostics

Author and Curator: Larry H. Bernstein, MD, FCAP


2.4.3 Diagnostics Computer-aided diagnostics

Back-to-Front Design

Robert Didner
Bell Laboratories

Decision-making in the clinical setting
Didner, R  Mar 1999  Amer Clin Lab

Mr. Didner is an Independent Consultant in Systems Analysis, Information Architecture (Informatics) Operations Research, and Human Factors Engineering (Cognitive Psychology),  Decision Information Designs, 29 Skyline Dr., Morristown, NJ07960, U.S.A.; tel.: 973-455-0489; fax/e-mail: bdidner@hotmail.com

A common problem in the medical profession is the level of effort dedicated to administration and paperwork necessitated by various agencies, which contributes to the high cost of medical care. Costs would be reduced and accuracy improved if the clinical data could be captured directly at the point they are generated in a form suitable for transmission to insurers or machine transformable into other formats. Such a capability could also be used to improve the form and the structure of information presented to physicians and support a more comprehensive database linking clinical protocols to outcomes, with the prospect of improving clinical outcomes. Although the problem centers on the physician’s process of determining the diagnosis and treatment of patients and the timely and accurate recording of that process in the medical system, it substantially involves the pathologist and laboratorian, who interact significantly throughout the in-formation-gathering process. Each of the currently predominant ways of collecting information from diagnostic protocols has drawbacks. Using blank paper to collect free-form notes from the physician is not amenable to computerization; such free-form data are also poorly formulated, formatted, and organized for the clinical decision-making they support. The alternative of preprinted forms listing the possible tests, results, and other in-formation gathered during the diagnostic process facilitates the desired computerization, but the fixed sequence of tests and questions they present impede the physician from using an optimal decision-making sequence. This follows because:

  • People tend to make decisions and consider information in a step-by-step manner in which intermediate decisions are intermixed with data acquisition steps.
  • The sequence in which components of decisions are made may alter the decision outcome.
  • People tend to consider information in the sequence it is requested or displayed.
  • Since there is a separate optimum sequence of tests and questions for each cluster of history and presenting symptoms, there is no one sequence of tests and questions that can be optimal for all presenting clusters.
  • As additional data and test results are acquired, the optimal sequence of further testing and data acquisition changes, depending on the already acquired information.

Therefore, promoting an arbitrary sequence of information requests with preprinted forms may detract from outcomes by contributing to a non-optimal decision-making sequence. Unlike the decisions resulting from theoretical or normative processes, decisions made by humans are path dependent; that is, the out-come of a decision process may be different if the same components are considered in a different sequence.

Proposed solution

This paper proposes a general approach to gathering data at their source in computer-based form so as to improve the expected outcomes. Such a means must be interactive and dynamic, so that at any point in the clinical process the patient’s presenting symptoms, history, and the data already collected are used to determine the next data or tests requested. That de-termination must derive from a decision-making strategy designed to produce outcomes with the greatest value and supported by appropriate data collection and display techniques. The strategy must be based on the knowledge of the possible outcomes at any given stage of testing and information gathering, coupled with a metric, or hierarchy of values for assessing the relative desirability of the possible outcomes.

A value hierarchy

  • The numbered list below illustrates a value hierarchy. In any particular instance, the higher-numbered values should only be considered once the lower- numbered values have been satisfied. Thus, a diagnostic sequence that is very time or cost efficient should only be considered if it does not increase the likelihood (relative to some other diagnostic sequence) that a life-threatening disorder may be missed, or that one of the diagnostic procedures may cause discomfort.
  • Minimize the likelihood that a treatable, life-threatening disorder is not treated.
  • Minimize the likelihood that a treatable, discomfort-causing disorder is not treated.
  • Minimize the likelihood that a risky procedure(treatment or diagnostic procedure) is inappropriately administered.
  • Minimize the likelihood that a discomfort-causing procedure is inappropriately administered.
  • Minimize the likelihood that a costly procedure is inappropriately administered.
  • Minimize the time of diagnosing and treating thepatient.8.Minimize the cost of diagnosing and treating the patient.

The above hierarchy is relative, not absolute; for many patients, a little bit of testing discomfort may be worth a lot of time. There are also some factors and graduations intentionally left out for expository simplicity (e.g., acute versus chronic disorders).This value hierarchy is based on a hypothetical patient. Clearly, the hierarchy of a health insurance carrier might be different, as might that of another patient (e.g., a geriatric patient). If the approach outlined herein were to be followed, a value hierarchy agreed to by a majority of stakeholders should be adopted.


Once the higher values are satisfied, the time and cost of diagnosis and treatment should be minimized. One way to do so would be to optimize the sequence in which tests are performed, so as to minimize the number, cost, and time of tests that need to be per-formed to reach a definitive decision regarding treatment. Such an optimum sequence could be constructed using Claude Shannon’s information theory.

According to this theory, the best next question to ask under any given situation (assuming the question has two possible outcomes) is that question that divides the possible outcomes into two equally likely sets. In the real world, all tests or questions are not equally valuable, costly, or time consuming; therefore, value(risk factors), cost, and time should be used as weighting factors to optimize the test sequence, but this is a complicating detail at this point.

A value scale

For dynamic computation of outcome values, the hierarchy could be converted into a weighted value scale so differing outcomes at more than one level of the hierarchy could be readily compared. An example of such a weighted value scale is Quality Adjusted Life Years (QALY).

Although QALY does not incorporate all of the factors in this example, it is a good conceptual starting place.

The display, request, decision-making relationship

For each clinical determination, the pertinent information should be gathered, organized, formatted, and formulated in a way that facilitates the accuracy, reliability, and efficiency with which that determination is made. A physician treating a patient with high cholesterol and blood pressure (BP), for example, may need to know whether or not the patient’s cholesterol and BP respond to weight changes to determine an appropriate treatment (e.g., weight control versus medication). This requires searching records for BP, certain blood chemicals (e.g., HDLs, LDLs, triglycerides, etc.), and weight from several

sources, then attempting to track them against each other over time. Manually reorganizing this clinical information each time it is used is extremely inefficient. More important, the current organization and formatting defies principles of human factors for optimally displaying information to enhance human information-processing characteristics, particularly for decision support.

While a discussion of human factors and cognitive psychology principles is beyond the scope of this paper, following are a few of the system design principles of concern:

  • Minimize the load on short-term memory.
  • Provide information pertinent to a given decision or component of a decision in a compact, contiguous space.
  • Take advantage of basic human perceptual and pat-tern recognition facilities.
  • Design the form of an information display to com-plement the decision-making task it supports.

F i g u re 1 shows fictitious, quasi-random data from a hypothetical patient with moderately elevated cholesterol. This one-page display pulls together all the pertinent data from six years of blood tests and related clinical measurements. At a glance, the physician’s innate pattern recognition, color, and shape perception facilities recognize the patient’s steadily increasing weight, cholesterol, BP, and triglycerides as well as the declining high-density lipoproteins. It would have taken considerably more time and effort to grasp this information from the raw data collection and blood test reports as they are currently presented in independent, tabular time slices.

Design the formulation of an information display to complement the decision-making task.

The physician may wish to know only the relationship between weight and cardiac risk factors rather than whether these measures are increasing or decreasing, or are within acceptable or marginal ranges. If so, Table 1 shows the correlations between weight and the other factors in a much more direct and simple way using the same data as in Figure 1. One can readily see the same conclusions about relations that were drawn from Figure 1.This type of abstract, symbolic display of derived information also makes it easier to spot relationships when the individual variables are bouncing up and down, unlike the more or less steady rise of most values in Figure 1. This increase in precision of relationship information is gained at the expense of other types of information (e.g., trends). To display information in an optimum form then, the system designer must know what the information demands of the task are at the point in the task when the display is to be used.

Present the sequence of information display clusters to complement an optimum decision-making strategy.

Just as a fixed sequence of gathering clinical, diagnostic information may lead to a far from optimum outcome, there exists an optimum sequence of testing, considering information, and gathering data that will lead to an optimum outcome (as defined by the value hierarchy) with a minimum of time and expense. The task of the information system designer, then, is to provide or request the right information, in the best form, at each stage of the procedure. For ex-ample, Figure 1 is suitable for the diagnostic phase since it shows the current state of the risk factors and their trends. Table 1, on the other hand, might be more appropriate in determining treatment, where there may be a choice of first trying a strict dietary treatment, or going straight to a combination of diet plus medication. The fact that Figure 1 and Table 1 have somewhat redundant information is not a problem, since they are intended to optimally provide information for different decision-making tasks. The critical need, at this point, is for a model of how to determine what information should be requested, what tests to order, what information to request and display, and in what form at each step of the decision-making process. Commitment to a collaborative relationship between physicians and laboratorians and other information providers would be an essential requirement for such an undertaking. The ideal diagnostic data-collection instrument is a flexible, computer-based device, such as a notebook computer or Personal Digital Assistant (PDA) sized device.

Barriers to interactive, computer-driven data collection at the source

As with any major change, it may be difficult to induce many physicians to change their behavior by interacting directly with a computer instead of with paper and pen. Unlike office workers, who have had to make this transition over the past three decades, most physicians’ livelihoods will not depend on converting to computer interaction. Therefore, the transition must be made attractive and the changes less onerous. Some suggestions follow:

  1. Make the data collection a natural part of the clinical process.
  2. Ensure that the user interface is extremely friendly, easy to learn, and easy to use.
  3. Use a small, portable device.
  4. Use the same device for collection and display of existing information (e.g., test results and his-tory).
  5. Minimize the need for free-form written data entry (use check boxes, forms, etc.).
  6. Allow the entry of notes in pen-based free-form (with the option of automated conversion of numeric data to machine-manipulable form).
  7. Give the physicians a more direct benefit for collecting data, not just a means of helping a clerk at an HMO second-guess the physician’s judgment.
  8. Improve administrative efficiency in the office.
  9. Make the data collection complement the clinical decision-making process.
  10. Improve information displays, leading to better outcomes.
  11. Make better use of the physician’s time and mental effort.


The medical profession is facing a crisis of information. Gathering information is costing a typical practice more and more while fees are being restricted by third parties, and the process of gathering this in-formation may be detrimental to current outcomes. Gathered properly, in machine-manipulable form, these data could be reformatted so as to greatly improve their value immediately in the clinical setting by leading to decisions with better outcomes and, in the long run, by contributing to a clinical data warehouse that could greatly improve medical knowledge. The challenge is to create a mechanism for data collection that facilitates, hastens, and improves the outcomes of clinical activity while minimizing the inconvenience and resistance to change on the part of clinical practitioners. This paper is intended to provide a high-level overview of how this may be accomplished, and start a dialogue along these lines.


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  5. Smith S, Mosier J. Guidelines for designing user interface soft-ware. ESD-TR-86-278, Aug 1986.
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Table 1

Correlation of weight with other cardiac risk factors

Cholesterol 0.759384
HDL 0.53908
LDL 0.177297
BP-syst. 0.424728
BP-dia. 0.516167
Triglycerides 0.637817

Figure 1  Hypothetical patient data.

(not shown)

Realtime Clinical Expert Support


Regression: A richly textured method for comparison and classification of predictor variables


Converting Hematology Based Data into an Inferential Interpretation

Larry H. Bernstein, Gil David, James Rucinski and Ronald R. Coifman
In Hematology – Science and Practice
Lawrie CH, Ch 22. Pp541-552.
InTech Feb 2012, ISBN 978-953-51-0174-1

A model for Thalassemia Screening using Hematology Measurements

https://www.researchgate.net/profile/Larry_Bernstein/publication/258848064_A_model_for_Thalassemia_Screening_using_Hematology_Measurements/links/0c9605293c3048060b000000.pdf A model for automated screening of thalassemia in hematology (math study).

Kneifati-Hayek J, Fleischman W, Bernstein LH, Riccioli A, Bellevue R.
Lab Hematol. 2007; 13(4):119-23. http://dx.doi.org:/10.1532/LH96.07003.

The results of 398 patient screens were collected. Data from the set were divided into training and validation subsets. The Mentzer ratio was determined through a receiver operating characteristic (ROC) curve on the first subset, and screened for thalassemia using the second subset. HgbA2 levels were used to confirm beta-thalassemia.

RESULTS: We determined the correct decision point of the Mentzer index to be a ratio of 20. Physicians can screen patients using this index before further evaluation for beta-thalassemia (P < .05).

CONCLUSION: The proposed method can be implemented by hospitals and laboratories to flag positive matches for further definitive evaluation, and will enable beta-thalassemia screening of a much larger population at little to no additional cost.

Measurement of granulocyte maturation may improve the early diagnosis of the septic state. Bernstein LH, Rucinski J. Clin Chem Lab Med. 2011 Sep 21;49(12):2089-95.
http://dx.doi.org:/10.1515/CCLM.2011.688. The automated malnutrition assessment.

David G, Bernstein LH, Coifman RR. Nutrition. 2013 Jan; 29(1):113-21.
http://dx.doi.org:/10.1016/j.nut.2012.04.017 Molecular Diagnostics

Genomic Analysis of Hematological Malignancies

Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy that occurs in children. Although more than 90% of children with ALL now survive to adulthood, those with the rarest and high-risk forms of the disease continue to have poor prognoses. Through the Pediatric Cancer Genome Project (PCGP), investigators in the Hematological Malignancies Program are identifying the genetic aberrations that cause these aggressive forms of leukemias. Here we present two studies on the genetic bases of early T-cell precursor ALL and acute megakaryoblastic leukemia.

  • Early T-Cell Precursor ALL Is Characterized by Activating Mutations
  • The CBFA2T3-GLIS2Fusion Gene Defines an Aggressive Subtype of Acute Megakaryoblastic Leukemia in Children

Early T-cell precursor ALL (ETP-ALL), which comprises 15% of all pediatric T-cell leukemias, is an aggressive disease that is typically resistant to contemporary therapies. Children with ETP-ALL have a high rate of relapse and an extremely poor prognosis (i.e., 5-year survival is approximately 20%). The genetic basis of ETP-ALL has remained elusive. Although ETP-ALL is associated with a high burden of DNA copy number aberrations, none are consistently found or suggest a unifying genetic alteration that drives this disease.

Through the efforts of the PCGP, Jinghui Zhang, PhD (Computational Biology), James R. Downing, MD (Pathology), Charles G. Mullighan, MBBS(Hons), MSc, MD (Pathology), and colleagues analyzed the whole-genome sequences of leukemic cells and matched normal DNA from 12 pediatric patients with ETP-ALL. The identified genetic mutations were confirmed in a validation cohort of 52 ETP-ALL specimens and 42 non-ETP T-lineage ALLs (T-ALL).

In the journal Nature, the investigators reported that each ETP-ALL sample carried an average of 1140 sequence mutations and 12 structural variations. Of the structural variations, 51% were breakpoints in genes with well-established roles in hematopoiesis or leukemogenesis (e.g., MLH2,SUZ12, and RUNX1). Eighty-four percent of the structural variations either caused loss of function of the gene in question or resulted in the formation of a fusion gene such as ETV6-INO80D. The ETV6 gene, which encodes a protein that is essential for hematopoiesis, is frequently mutated in leukemia. Among the DNA samples sequenced in this study, ETV6 was altered in 33% of ETP-ALL but only 10% of T-ALL cases.

Next-generation sequencing in hematologic malignancies: what will be the dividends?

Jason D. MerkerAnton Valouev, and Jason Gotlib
Ther Adv Hematol. 2012 Dec; 3(6): 333–339.

The application of high-throughput, massively parallel sequencing technologies to hematologic malignancies over the past several years has provided novel insights into disease initiation, progression, and response to therapy. Here, we describe how these new DNA sequencing technologies have been applied to hematolymphoid malignancies. With further improvements in the sequencing and analysis methods as well as integration of the resulting data with clinical information, we expect these technologies will facilitate more precise and tailored treatment for patients with hematologic neoplasms.

Leveraging cancer genome information in hematologic malignancies.

Rampal R1Levine RL.
J Clin Oncol. 2013 May 20; 31(15):1885-92.

The use of candidate gene and genome-wide discovery studies in the last several years has led to an expansion of our knowledge of the spectrum of recurrent, somatic disease alleles, which contribute to the pathogenesis of hematologic malignancies. Notably, these studies have also begun to fundamentally change our ability to develop informative prognostic schema that inform outcome and therapeutic response, yielding substantive insights into mechanisms of hematopoietic transformation in different tissue compartments. Although these studies have already had important biologic and translational impact, significant challenges remain in systematically applying these findings to clinical decision making and in implementing new technologies for genetic analysis into clinical practice to inform real-time decision making. Here, we review recent major genetic advances in myeloid and lymphoid malignancies, the impact of these findings on prognostic models, our understanding of disease initiation and evolution, and the implication of genomic discoveries on clinical decision making. Finally, we discuss general concepts in genetic modeling and the current state-of-the-art technology used in genetic investigation.

p53 mutations are associated with resistance to chemotherapy and short survival in hematologic malignancies

E Wattel, C Preudhomme, B Hecquet, M Vanrumbeke, et AL.
Blood, (Nov 1), 1994; 84(9): pp 3148-3157

We analyzed the prognostic value of p53 mutations for response to chemotherapy and survival in acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and chronic lymphocytic leukemia (CLL). Mutations were detected by single-stranded conformation polymorphism (SSCP) analysis of exons 4 to 10 of the P53 gene, and confirmed by direct sequencing. A p53 mutation was found in 16 of 107 (15%) AML, 20 of 182 (11%) MDS, and 9 of 81 (11%) CLL tested. In AML, three of nine (33%) mutated cases and 66 of 81 (81%) nonmutated cases treated with intensive chemotherapy achieved complete remission (CR) (P = .005) and none of five mutated cases and three of six nonmutated cases treated by low-dose Ara C achieved CR or partial remission (PR) (P = .06). Median actuarial survival was 2.5 months in mutated cases, and 15 months in nonmutated cases (P < lo-‘). In the MDS patients who received chemotherapy (intensive chemotherapy or low-dose Ara C), 1 of 13 (8%) mutated cases and 23 of 38 (60%) nonmutated cases achieved CR or PR (P = .004), and median actuarial survival was 2.5 and 13.5 months, respectively (P C lo-’). In all MDS cases (treated and untreated), the survival difference between mutated cases and nonmutated cases was also highly significant. In CLL, 1 of 8 (12.5%) mutated cases treated by chemotherapy (chlorambucil andlor CHOP andlor fludarabine) responded, as compared with 29 of 36 (80%) nonmutated cases (P = .02). In all CLL cases, survival from p53 analysis was significantly shorter in mutated cases (median 7 months) than in nonmutated cases (median not reached) (P < IO-’). In 35 of the 45 mutated cases of AML, MDS, and CLL, cytogenetic analysis or SSCP and sequence findings showed loss of the nonmutated P53 allele. Our findings show that p53 mutations are a strong prognostic indicator of response to chemotherapy and survival in AML, MDS, and CLL. The usual association of p53 mutations to loss of the nonmutated P53 allele, in those disorders, ie, to absence of normal p53 in tumor cells, suggests that p53 mutations could induce drug resistance, at least in part, by interfering with normal apoptotic pathways in tumor cells.

Genomic approaches to hematologic malignancies

Benjamin L. Ebert and Todd R. Golub
Blood. 2004; 104:923-932

In the past several years, experiments using DNA microarrays have contributed to an increasingly refined molecular taxonomy of hematologic malignancies. In addition to the characterization of molecular profiles for known diagnostic classifications, studies have defined patterns of gene expression corresponding to specific molecular abnormalities, oncologic phenotypes, and clinical outcomes. Furthermore, novel subclasses with distinct molecular profiles and clinical behaviors have been identified. In some cases, specific cellular pathways have been highlighted that can be therapeutically targeted. The findings of microarray studies are beginning to enter clinical practice as novel diagnostic tests, and clinical trials are ongoing in which therapeutic agents are being used to target pathways that were identified by gene expression profiling. While the technology of DNA microarrays is becoming well established, genome-wide surveys of gene expression generate large data sets that can easily lead to spurious conclusions. Many challenges remain in the statistical interpretation of gene expression data and the biologic validation of findings. As data accumulate and analyses become more sophisticated, genomic technologies offer the potential to generate increasingly sophisticated insights into the complex molecular circuitry of hematologic malignancies. This review summarizes the current state of discovery and addresses key areas for future research. Flow cytometry

Introduction to Flow Cytometry: Blood Cell Identification

Dana L. Van Laeys

No other laboratory method provides as rapid and detailed analysis of cellular populations as flow cytometry, making it a valuable tool for diagnosis and management of several hematologic and immunologic diseases. Understanding this relevant methodology is important for any medical laboratory scientist.

Whether you have no previous experience with flow cytometry or just need a refresher, this course will help you to understand the basic principles, with the help of video tutorials and interactive case studies.

Basic principles include:

  1. Immunophenotypic features of various types of hematologic cells
  2. Labeling cellular elements with fluorochromes
  3. Blood cell identification, specifically B and T lymphocyte identification and analysis
  4. Cell sorting to isolate select cell population for further analysis
  5. Analyzing and interpreting result reports and printouts

Read Full Post »

Larry, H. Bernstein, MD, FCAP, Author and Curator


Picking the Lock on Pluripotency

Kevin Eggan, Ph.D.
N Engl J Med 28 Nov 2013; 369:2150-2151 http://dx.doi.org/10.1056/NEJMcibr1311880

Induced pluripotent stem (iPS) cells are obtained by reprogramming somatic cells. This powerful disease-modeling research tool is central to certain experimental approaches to therapy. A recent study showed that iPS cells can be generated with a very high degree of efficiency.

FIGURE 1  http://dx.doi.org/nejmcibr1311880_f1

nejmcibr1311880_f1   A Potent Provision of Induced Pluripotent Stem Cells.

A Potent Provision of Induced Pluripotent Stem Cells

Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors

AA Mack, S Kroboth,  D Rajesh,  Wen Bo Wang
Published: November 22, 2011  PLoS ONE 6(11): e27956   http://dx.doi.org/10.1371/journal.pone.0027956

The methodology to create induced pluripotent stem cells (iPSCs) affords the opportunity to generate cells specific to the individual providing the host tissue. However, existing methods of reprogramming as well as the types of source tissue have significant limitations that preclude the ability to generate iPSCs in a scalable manner from a readily available tissue source. We present the first study whereby iPSCs are derived in parallel from multiple donors using episomal, non-integrating, oriP/EBNA1-based plasmids from freshly drawn blood. Specifically, successful reprogramming was demonstrated from a single vial of blood or less using cells expressing the early lineage marker CD34 as well as from unpurified peripheral blood mononuclear cells. From these experiments, we also show that proliferation and cell identity play a role in the number of iPSCs per input cell number. Resulting iPSCs were further characterized and deemed free of transfected DNA, integrated transgene DNA, and lack detectable gene rearrangements such as those within the immunoglobulin heavy chain and T cell receptor loci of more differentiated cell types. Furthermore, additional improvements were made to incorporate completely defined media and matrices in an effort to facilitate a scalable transition for the production of clinic-grade iPSCs.

Citation: Mack AA, Kroboth S, Rajesh D, Wang WB (2011) Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors. PLoS ONE 6(11): e27956. doi:10.1371/journal.pone.0027956


Fibroblasts have been a predominate source material for the development of the process to generate induced pluripotent stem cells (iPSCs) given their ability to expand, endure for multiple passages in culture, and receptiveness to efficient infection by viruses expressing a combination of transcription factors for reprogramming [1]–[6]. However, fibroblasts from skin biopsies require invasive surgical procedures, are labor intensive and isolating a sufficient number for reprogramming takes time. In addition, the ability to generate iPSCs from skin appears inversely correlated with the age of the donor likely due to increasing exposure to external mutagens [7]. There is value, therefore, in alternative tissue sources to generate iPSCs that minimize the risk for additional mutation, involve less invasive procedures, and are amenable to industrialization to increase availability across an extensive population range.


A blood draw is an ideal starting point to generate donor-specific iPSCs because it is minimally invasive and established procedures are already in place for acquisition and handling [8]. Lymphocytes comprise a large fraction of the peripheral blood mononuclear cell (PBMC) population but pose at least two potential limitations. First, they are subject to intrinsic DNA rearrangements such as those that occur in B and T cells at the V, D, and J gene segments as well as T cell receptor (TCR) loci to generate a diverse repertoire of antigen-specific surface immunoglobulins. These rearrangements are subsequently perpetuated in iPSCs generated from them and their impact on iPSC function is currently unknown [9], [10]. Second, some research has indicated that host cell types may influence functional properties of iPSCs [11], [12]. For example, while embryonic stem (ES) cells and progenitor cells derived from bone marrow successfully differentiate into B cells, iPSCs derived from B cells have demonstrated resistance to this ability [13], [14], [15]. Therefore, choosing an early lineage cell type that lacks DNA rearrangements alleviates the potential risk of reduced ability to differentiate.

Somatic cells that are characteristically more progenitor-like with respect to the expression of early lineage markers, such as CD34, appear more susceptible to reprogramming, and they too can be isolated from blood [16]. For example, Haase and colleagues successfully isolated and reprogrammed early progenitor cells isolated from cord blood. The ability to reprogram cells from peripheral blood, however, expands the range of host cells available for reprogramming especially when acquisition of cord blood-derived material is not an option. However, the amount of CD34+ cells represents less than 0.1% of the population of PBMCs thus limiting the amount of source material available for reprogramming. To generate enough starting material to perform reprogramming trials, Loh and colleagues relied on patients treated with granulocyte-colony stimulating factor (G-CSF) to expand the number of CD34+ cells in circulating peripheral blood and ultimately generated iPSCs from these cells [17]. The acquisition of blood that does not require donors to receive these agents would be more desirable to avoid the negative side effects associated with them [18]. Studies have also shown that cells from mobilized blood demonstrate functional differences when compared with cells from non-mobilized samples indicating changes to properties intrinsic to the cell [19]. For example, epigenetic and genetic anomalies (i.e. aneuploidy) have been detected in cells derived from patients mobilized with G-CSF [20], [21]. These observations increase the likelihood that similar genetic modifications would carry over into iPSCs generated from mobilized CD34+ cells and potentially impact their function.

The method to generate iPSCs from cord and mobilized peripheral blood has predominately relied on viral-based methods to introduce reprogramming factors [16], [17]. Resulting clones thus have transgenes integrated into their genomes that may alter the function of iPSCs, increase the risk of cancer, and hinder their potential for clinical application. Improvements to reprogramming methods have been made to eliminate integrated transgenes including a recent study examining the reprogramming potential of blood-derived cells using a previously described episomal, oriP/EBNA1-based transfection method [2], [22], [23]. We capitalized on the oriP/EBNA1-method but made modifications to accommodate the reprogramming of CD34+ cells derived from actual vials of blood collected across multiple donors. The oriP/EBNA1-based vectors contribute to the replication and retention of plasmids during each cell division long enough for reprogramming to occur and are lost over time resulting in cells free of transfected DNA and integrated transgenes [24], [25]. Importantly, reprogramming can be achieved through a single transfection. Herein we demonstrate the ability to generate iPSCs from a single vial of blood or less using an improved process of reprogramming that incorporates fully defined conditions to generate iPSCs free of gene rearrangements and transgene elements.



Hematopoietic progenitor cells from non-mobilized, peripheral blood are expandable


Hematopoietic progenitor cells expressing CD34 represent a small fraction of the population and limit the number of cells available for reprogramming; therefore, a modified formulation of a media used to expand cord blood cells was tested on CD34+ cells isolated from peripheral blood [26]. CD34+ cells from two non-mobilized, peripheral (PB.1 and PB.2) blood donors were tested in comparison to CD34+ cells from two cord blood donors (CB.1 and CB.2). Cells were placed into untreated, 24-well culture plates at 1.2×104 per ml of expansion media (StemSpan basal medium; 300 ng/ml each of SCF, Flt3, TPO; 100 ng/ml IL-6; 10 ng.ml IL-3) and fed 3 to 4 days later. The total number of cells from peripheral blood expanded 170-fold and 680-fold from cord blood after 10 to 14 days in culture (Figure 1A, left hand panel). The percentage of CD34+ cells in the peripheral and cord blood samples peaked in less than 1 week (CB data not shown; Figure 1A, right hand panel). The expression profile of expanding populations was determined by flow cytometry for all donors examined herein (Figure 1B). Phenotypic analysis of the resulting peripheral blood culture PB.2 after 10 days of expansion revealed predominately early progenitor cells expressing CD43, CD45, CD33, CD44, CD15, and CD117 and marginal levels of T (CD3, CD4, CD8), NK (CD56, CD94), B (CD19), macrophage (CD163), megakaryocyte (CD41), and monocyte (CD14) cells (Figure 1C).

Figure 1. Hematopoietic cells enriched for CD34 expression are expandable.

journal.pone.0027956.g001  Figure 1. Hematopoietic cells enriched for CD34 expression are expandable


A. Graphs depict the expansion of purified cells from either two peripheral (PB.1 and PB.2) or cord (CB.1 or CB.2) blood donors over time (lefthand panel) along with the percentages of the total population that are CD34+ (righthand panel). B. Representative profile of a purified population of cells after 6 days of expansion by flow cytometry on cells isolated from Donor 3002. Flow cytometry plots from control staining using IgG antibodies (upper plots) are compared to plots with antibodies specific to lineage markers (lower plots). C. The graph represents an extended analysis by flow cytometry of the characteristic profile of PB.2 cells after 10 days of expansion. The % positive indicates the fraction of the population expressing the cell surface markers on the x-axis. D. The total number of CD34+ cells across 16 different donors was assessed beginning at 0 and 6 days of expansion (left side y-axis). The fold expansion (right side y-axis, orange squares) was determined by dividing the total number of cells at day 6 divided by the number of cells at day 0 after purification. The average percent of CD34 expression across all 16 donors was 48+/−19%.     http://dx.doi.org/10.1371/journal.pone.0027956.g001

These expansion conditions were then applied to cells acquired from a single vial of blood collected from multiple donors. PBMCs were isolated, frozen down immediately, or directly purified for CD34+ cells and seeded for expansion. On average, 1×107 PBMCs were recovered per 8 ml vial of blood and yielded approximately 2×104 cells after purification for CD34-expressing cells (data not shown). Although the magnitude of expansion was variable, cells from all of the donors demonstrated expansion ranging from 3 to 83-fold after 6 days in culture and approximately 48+/−19% of that population expressed CD34 (Figure 1D).

Optimizing the generation of iPSCs with small molecules and a defined matrix

The total number of purified cells isolated from a single, 8 ml vial of blood can be as little as 2×104 CD34+ cells; therefore, a range of CD34+ cell numbers was tested to determine transfection efficiency from low cell numbers. The efficiency of transfection was determined by transfecting an oriP/EBNA1-containing plasmid encoding GFP into expanded CD34+ cells and assessing them by flow cytometry. Viability was determined by identifying the fraction of viable cells that did not stain positively for trypan blue the day after transfection divided by the total number of input cells. Viability was approximately 30% when 1×104 to 1×105 input cells were used for transfection (data not shown). Cell numbers at 1×104 and 3×104 resulted in an efficiency of 30% and was 40% when using 6×104 and 1×105 cells (Figure 2A). Cells expanded for only 3 days demonstrated a two-fold increase in transfection efficiency. Over 90% of those cells also co-expressed GFP and CD34 while only 18% of the cells transfected after 6 days of expansion co-expressed both markers (Figure 2B, C). These results support the notion that the conditions selected for this protocol favor the transfection of CD34+ cells present in the population.

Figure 2. Identifying optimal transfection conditions for CD34+ cells.

journal.pone.0027956.g002   Figure 2. Identifying optimal transfection conditions for CD34+ cells.

A. PBMCs (donor GG) were isolated and purified for CD34-expression and expanded for 6 days. A range of cell numbers were transfected with a control, oriP/EBNA1-based plasmid expressing GFP. Transfection efficiency was determined by calculating the percentage of viable cells expressing GFP detectable by flow cytometry (n = 6). B. PBMCs (donor A2389) were isolated, purified for CD34-expression and expanded for 3 or 6 days. 6×104 to 1×105 cells were transfected with the control, GFP-expressing plasmid. The graph depicts the percent of the total population that is GFP-positive along with the absolute number of total cells (n = 3). C. The graph represents the fraction of cells in B that co-express GFP and CD34 when transfected at 3 or 6 days of expansion (n = 3).    http//dx.doi.org/10.1371/journal.pone.0027956.g002

We anticipated variability in reprogramming efficiency given the differences already observed across donors for other cell types tested and with other methods of reprogramming. Therefore, we optimized the matrix, media, and plasmid combinations used for reprogramming. Firstly, a common source of variation occurs when MEFs or matrigel are used because both are undefined, cumbersome to prepare, and vary from lot-to-lot. Therefore, we established a defined matrix by testing a variety of commercially available possibilities. Recombinant protein fragments containing the active domains of human fibronectin (RetroNectin) or vitronectin consistently supported iPSC formation the best among those tested. Second, the efficiency of colony formation on RetroNectin-coated plates improved significantly when used in combination with StemSpan SFEM media, N2, B27, and a cocktail of small molecules that included PD0325901, CHIR99021, A-83-01, and HA-100 (Figure 3A). These molecules have been described previously as inhibitors of MEK, GSK3β, TGFβ, and ROCK pathways, respectively [27], [28]. Patches of adherent cells appeared within one week and became a positive indicator for progression into iPSCs since hematopoietic cells are typically cultured in suspension. The following week many of the colonies exhibited overt characteristics typical of an iPSC and stained positively for the common pluripotency markers Tra-1-81 and alkaline phosphatase (AP) (Figure 3A). The borders of the colonies were compact and the nucleoli more visible when cultures were transitioned to defined, TeSR2 media without small molecules 1.5 to 2 weeks following transfection. Thirdly, episomal oriP/EBNA1-based plasmids were used to deliver Oct4, Sox2, Klf4, C-myc, Nanog, Lin28, and SV40 Large T-antigen as previously described (Set 1; Figure 3B) [2]. Different combinations of reprogramming plasmids were also tested to determine whether a boost in reprogramming efficiency was possible. Based on previous reports indicating the benefit of L-myc in reprogramming trials, we modified plasmid combination Set 1 and substituted L-myc in place of C-myc (Set 2, Figure 3B) [29], [30]. While an improvement was not observed when C-myc was substituted for L-myc in the combination of plasmids represented in Set 1 (data not shown), an equal to or two-fold improvement was observed with plasmid Set 2 expressing L-myc (Figure 3C). Optimizing a range of input cell numbers for transfection also revealed more consistent generation of iPSCs when transfecting greater than 5×104 cells (Figure 3D).

Figure 3. Plasmid transfections to optimize reprogramming efficiency.

journal.pone.0027956.g003  Figure 3. Plasmid transfections to optimize reprogramming efficiency.

A. Representative reprogramming trial from freshly drawn blood (donor 3002) using combination plasmid Set 2 for transfection. A single well is shown from a 6-well plate that contains colonies staining positively for AP activity (i). The white arrowhead highlights the colony magnified in panel ii that also stained positively for Tra-1-81 expression (green), panel iii. B. Schematic of the plasmid sets successfully used for reprogramming trials. Set 1 contains a combination of two plasmids for transfection whereby a 20 kb plasmid that either contains C- or L-myc is depicted. Set 2 includes a three plasmid combination for transfection. C. CD34+ cells purified from four different donors were expanded for 6 days and transfected using the plasmid combination that expresses either Set 1 or Set 2 plasmid sets to compare the total number of resulting iPSCs. D. Reprogramming trials were performed using plasmid Set 2 to transfect a range of cell numbers expanded for 6 days (donor GG, n = 6).  http://dx.doi.org/10.1371/journal.pone.0027956.g003


Optimization of iPSCs generated from CD34+ cells isolated from fresh, whole blood across multiple donors

The next step was to confirm iPSCs could be generated from actual vials of human blood, ensure cell numbers optimized for expansion and transfection are applicable across multiple donors, and determine whether the starting volume of blood can be minimized. Colonies emerging during reprogramming were scored positive by their ability to express Tra1-81 and exhibit a classic embryonic stem (ES) cell-like morphology. After colonies were picked from the reprogramming cultures, a subset of them were further characterized to confirm their pluripotency. Reprogramming efficiency was calculated in two ways 1) the number of iPSCs divided by the total volume of blood collected from each donor and 2) the total number of iPSCs divided by the number of cells for transfection multiplied by 1×105 cells. The first calculation incorporates the whole process beginning from the blood collection to the generation of an iPSC. The second calculation removes the variability incurred during the isolation of PBMCs, purification, and expansion and focuses on the number of iPSCs generated per number of cells placed into transfection.

We tested blood collected across donors spanning a range of ethnicities, ages, and genders to confirm iPSCs could be generated from CD34+ cells purified from fresh blood draws (Table 1). Six of these donors provided up to 55 ml of blood, and PBMCs from them were either isolated and used directly for purification, expansion, and reprogramming or frozen down after isolation. iPSCs were successfully generated from all six donors regardless of whether they were from fresh or frozen cells despite the lower efficiency of reprogramming, less than 1 iPSC per ml of blood (Table 2). Next, smaller volumes of blood representative of a single vial were obtained from six different donors to test parameters established in earlier experiments such as the number of cells for transfection and plasmid combinations. The cell numbers used for transfection from donors 3052, 3233, and 3373 ranged from 2×104 to 4×104 which fall below the minimum, 5×104 cells, established with our optimization studies. These experiments resulted in less than one iPSC per ml of blood (Table 2). Also, transfections with cells from donors 2583, 2970, and 3185 represent early trials performed with plasmid Set 1 expressing C-myc before Set 2 plasmids expressing L-myc were fully optimized which may have resulted in more iPSCs.

Table 1. Diversity across the set of donors used for reprogramming trials to generate iPSCs.

Table 2. Optimizing reprogramming from a range of blood volumes across multiple donors.

The next step was to then extend the insights acquired from these donors and verify the robustness of our protocol against ten new donors. The average number of iPSCs per ml of blood and per 1×105 cells across these donors indeed improved after incorporating experience with handling and the testing performed on the earlier donor samples (Table 3). Furthermore, these experiments were extended to test even smaller volumes of blood from a subset of the same donors in Table 3. CD34+ cells purified from approximately 4 ml of blood were sufficient to generate iPSCs from all six donors tested, and CD34+ cells from approximately 2 ml of blood from four out of six of these donors generated iPSCs (Table 4). These results demonstrate that iPSCs can be generated from CD34+ isolated from tractable volumes of blood using this non-intergrating and feeder-free method of reprogramming.

Table 3. Improved reprogramming across multiple donors from a single vial of blood.


Table 4. The efficiency of reprogramming CD34+ cells beginning from 4 ml of blood or less.


iPSCs derived from peripheral blood are pluripotent and free of transgene elements

Multiple iPSCs from each of the donors that were reprogrammed from Tables 2, 3, and 4 were selected for further characterization to confirm their pluripotency. The clones exhibited a normal karyotype, were positive for Tra-1-81 and SSEA-4 expression by flow cytometry as well as endogenous genes DNMT3B, REX1, TERT, UTF1, Oct4, Sox2, Nanog, Lin28, Klf4, and C-myc (Table 5, Figure 4A–C). Clones did not exhibit integrated transgene or episomal elements and loss of episomal DNA occurred, on average, within 7–10 passages (Table 5, Figure 4D,E). A PCR screen did not reveal rearrangements pertaining to immunoglobulin heavy chain (IgH) or a subset of T cell receptor (TCR) gene segments (Table 5, Figure 4F). The lack of rearrangements supports the notion that the protocol selectively favors the production of iPSCs from hematopoietic progenitors rather than more differentiated cell types. When used for in vitro directed differentiation at passage 15, donor 2939 iPSC clones 4 and 5, which have lost episomal plasmids, were competent to form neurons (Figure 5G). Furthermore, five iPS clones from three different donors also formed teratomas after injection into immunodeficient (SCID) mice (Figure 4G). Interestingly, the presence of residual episomal plasmids did not appear to hinder the ability to form teratomas since clone 6 from donor 2970 did not lose transfected plasmids until passage 18, well after injection into mice for teratoma studies.

Figure 4. Characterization of iPSCs derived from CD34+ blood cells.

journal.pone.0027956.g004  Figure 4. Characterization of iPSCs derived from CD34+ blood cells.

A subset of iPSC clones were characterized for pluripotency. The experiments demonstrated in this figure provide representative examples of the types of results observed for characterization studies using iPS clones 4 and/or 5 derived from donors 2939 and 3389. A. Cytogenetic analysis on G-banded metaphase cells from iPS clone 4 exhibiting a normal karyotype. B and C. RT-PCR confirms the endogenous expression of classic pluripotency genes and the absence of expression from transgenes. A standard in-house iPS line served as the positive control k. D. Clones were deemed free of episomal (E) DNA and genomic integration (G) by PCR. E. PCR was used to track the loss of oriP/EBNA1-based plasmids at multiple passages using primers that amplify EBNA1. A control plasmid at 1 and 20 copies per genome was used to establish the sensitivity of the PCR at 1 copy per 3,000 cells. F. PCR screen using primers specific for the joining region and all three of the conserved framework regions (FR1, FR2 and FR3) to amplify immunoglobulin heavy chain (IgH) gene rearrangements and two assays with primers specific to the T cell receptor (TCR) gamma gene rearrangement. G. Representative image of donor 2939 clone 5 differentiated in vitro into neurons (i). Clone 5 also demonstrated differentiation into all three germ layers: ii) epithelium iii) endoderm iv) mesoderm v) ectoderm vi) endoderm from teratomas formed when iPSCs were injected into immunodeficient, SCID mice.

Figure 5. The presence of CD34+ cells correlates with reprogramming efficiency.

journal.pone.0027956.g005  Figure 5. The presence of CD34+ cells correlates with reprogramming efficie

A. CD34+ cells from four different blood donors were expanded for 3, 6, 9, or 13 days. A large volume of blood was collected from donors 3096, 2849, and 3389 to ensure sufficient cell numbers to perform these studies. Expanding CD34+ cells using plasmid DNA combination Set 2. The efficiency of reprogramming was calculated as the total number of iPSCs exhibiting morphological features characteristic of an ES cell and an ability to stain positively for Tra-1-81 divided by the total number of cells used for transfection. Black Squares depict the percentage of the population expressing CD34 at the indicated days of expansion. B. Representative reprogramming trial whereby both the positive (i) and negative (ii) fraction following purification were used for reprogramming. Panel (i) shows one well of a 6-well plate that contains successfully reprogrammed colonies from donor 2939 based on their ability to demonstrate AP activity. The CD34-depleted fraction from donor 2939 was unable to form colonies as indicated by the lack of AP staining when performed in parallel with the purified population panel, ii. Panels iii and iv magnify the colony in panel (i) marked by a white arrowhead and demonstrates expression of Tra-1-81 (green), panel iv.

Table 5. Characterization of insert-free iPS clones derived from fresh blood.

Reprogramming efficiency correlates with the amount of CD34-expression

The isolation of CD34+ cells from PBMCs creates an additional step in our process and others have demonstrated successful reprogramming directly from PBMCs without the need for purification [22]. Therefore, several experiments to determine whether a correlation exists between CD34+ cells and reprogramming efficiency were performed. First, the expanding CD34+ populations were screened by flow cytometry for characterization prior to transfection. T, B, and NK cells were undetectable after 3 and 6 days of expansion demonstrated by their lack of CD3, 19, and 56 expression, respectively. The percentage of CD34 expression during expansion ranged from 30 to 100%, thus increasing the likelihood of reprogramming more of an early lineage cell type (n = 9, data not shown). Second, samples were taken from the purified populations during expansion at different timepoints as they lost CD34 expression to determine their receptiveness to reprogramming. A decrease in reprogramming efficiency was observed in correlation with decreasing percentages of CD34 expression across populations of cells from four independent donors (Figure 5A). For example, donor 3096 exhibited only 1 iPSC per 1×105 input cells when beginning from cells expanded for 13 days (31% CD34+) compared to 91.5 iPSCs per 1×105 cells following 3 days when levels of CD34 expression were much higher, 98% (Figure 5B). Third, populations depleted of CD34+ cells were tested for their ability to reprogram in parallel with their CD34+ cell counterparts. These CD34-depleted populations were not receptive to reprogramming as the CD34+ cells even when the same media and transfection conditions were used (n = 3, Figure 5B). Finally, a side-by-side comparison of reprogramming efficiency was performed between unpurified PBMCs and CD34+ cells isolated from 10 different donors. A medium described previously for the expansion of erythroblasts and for successful reprogramming studies was used to ensure media would not be a limiting factor for reprogramming the PBMCs in our protocol [22], [31]. Reprogramming trials beginning from either PBMCs or CD34+ cells were launched in parallel using their respective media for expansion. The efficiency of reprogramming was approximately 2 to 8 fold higher when beginning with cells purified for CD34 expression in 9 out of the 10 donors compared to those from PBMCs (p = 0.007; Figure 6). A significant fraction of the PBMC population was comprised of lymphocytes (~79+/−14% CD3/CD19) at the time of transfection (data not shown); therefore, PCR was performed to screen for potential IgH and TCR gene rearrangements to determine whether both protocols promoted the generation of iPSCs free of gene rearrangements. Interestingly, screened clones from either cell type were free of IgH and TCR gene rearrangements indicating that both protocols favor the reprogramming of early progenitor cells. The lower efficiency of reprogramming from the PBMC population may reflect the dilution of early progenitor blood cells by a predominately lymphocytic population.

Figure 6. Comparing the efficiency of reprogramming between PBMCs and CD34+ cells.

PBMCs and CD34+ cells were isolated from single tubes of blood provided from 10 different donors. The efficiency of reprogramming following transfection with DNA Combination Set 2 was determined for each donor and each method. “Total colonies” refers to all iPS colonies derived from either CD34+ or PBMC populations that stain positively for Tra1-60 and “iPS-like” colonies are those that stain positively for Tra1-60, exhibit clear iPS morphology, and are large enough to pick for expansion. Input cells refer to the number of CD34+ cells or PBMCs were used for transfection. The efficiencies across all donors from both methods were compared using the Wilcoxon signed rank test (two-sided), p = 0.007.

Generation of iPSCs from CD34+ cells using completely defined reagents

Additional reprogramming trials were performed using completely defined conditions to enable the production of clinic-grade iPSCs. A large pool of CD34+ cells mixed from multiple donors was used for multiple tests and resulted in a successful expansion of 113+/−11 fold in defined media compared to 83+/−32 fold for cells in standard conditions after 6 days of expansion (Figure 7A). Despite the 30-fold difference between the two conditions, the absolute number of CD34+ cells is similar between the two populations when multiplied by the percentage of the population expressing CD34 by flow cytometry. For example, 42+/−13% of the population expanded in standard conditions expressed CD34 and 26+/−16% expressed CD34 using completely defined conditions (Figure 7A). There were no detectable CD3+, CD19+, or CD56+ cells after 6 days in culture consistent with our earlier expansion trials (data not shown). The media used for reprogramming is completely defined with the exception of the supplement B27 which contains bovine serum albumin (BSA). However reprogramming was still achieved in the presence or absence of B27 (Figure 7B). These improvements coupled with a defined matrix enables the production of iPSCs in a completely defined process.

Figure 7. The generation of iPSCs from blood using completely defined conditions.


A. Fold expansion of CD34+ cells pooled from multiple blood donors in standard (n = 13) and completely defined conditions (n = 2) after 6 days of expansion. Fold expansion was calculated from the total number of cells at day 6 divided by the number of cells the day after purification. Percentages indicate the fraction of cells expressing CD34 in the total population as assessed by flow cytometry. B. Reprogramming trials were performed on CD34+ cells obtained by leukapheresis from donors GG and A2389 with and without the B27 supplement.


CD34+ cells possess characteristics that make them an ideal blood cell to reprogram: they are readily identified, highly receptive to reprogramming, and free of gene rearrangements characteristic of more differentiated cell types. However, their low numbers in circulating blood have made them a less desirable cell type for reprogramming because large volumes of blood were predicted to be required for the generation of iPSCs. We describe a method to generate insert-free iPSCs from CD34+ cells beginning from a single vial of blood or less. In some cases, the number of CD34+ cells that expanded exceeded that required for a single transfection after 6 days in culture making it possible to transfect after only 3 days of expansion, shorter than the amount of time Chou and colleagues used to reprogram unpurified PBMCs [22].

Proliferation contributes to reprogramming efficiency; therefore, choosing a culture medium that promotes proliferation will effectively promote reprogramming. Data presented in our manuscript supports this assertion because all four donor populations examined were almost 100% positive for CD34 expression after 3 days in culture, but they were not equally receptive to reprogramming (Figure 5A). Cells from donor 3096 demonstrated the highest fold expansion following purification as well as the highest efficiency of reprogramming. However, we go on to demonstrate that proliferation is not the only contributor to efficient reprogramming. In our experiments, the purity of the cell population diminishes over time following purification, but the cells within the culture continue to expand despite low CD34 expression. Figure 1C shows that this expanding population 10 days following purification consists primarily of early lineage progenitor cells. This result indicates that our medium has the capacity to stimulate the proliferation of non-T and non-B cells that have never been or are no longer CD34+. If proliferation were the primary force driving the efficiency of reprogramming, then it would be expected that proliferating non-T/non-B cells within our population would be equally receptive to reprogramming regardless of their time in culture. Our results are contrary to this hypothesis, however, because the efficiency of reprogramming decreases as the magnitude of CD34 expression decreases. This is observed across four independent donor cell populations and is consistent with dependence of reprogramming on CD34 expression (see Figure 5A). These results, taken together, support our hypothesis that both proliferation and cell identity contribute to the efficiency of reprogramming.

We have also outlined a protocol that begins to systematically address some of the challenges in the generation of clinical-grade iPSCs in an effort to advance their use from research lab to clinic. iPSCs must not only be manufactured consistently from a tractable tissue source but also satisfy safety requirements. The core of these requirements includes the use of completely defined culture conditions and a standardized reprogramming method that results in the removal of the potentially oncogenic transgenes employed to reprogram the cells. The starting point of the protocol is the actual patient sample, a single vial of blood. The ability to begin from frozen rather than fresh starting material allows flexibility to launch multiple reprogramming trials in parallel. We demonstrate that either CD34+ cells or PBMCs may be used as the source population for reprogramming. The iPSCs generated by this method are free of transfected DNA as well as B and T cell gene rearrangements. Several challenges remain, however, before routine production of clinical-grade iPSCs can be completely performed. First, there is considerable variation in the efficiency of reprogramming from donor to donor. Some of this variation is likely due to the inherent differences among the donors, but a careful examination of external sources of variation at each step from blood to iPSCs may well reveal areas in addition to those we have uncovered that can be better controlled. For example, we demonstrate potential in the ability to produce iPSCs using completely defined reagents to minimize variation. Second, an automated method for screening and selecting iPSCs during reprogramming would facilitate high throughput production of iPSCs. Third, a robust production protocol must also include a method for the rapid screening of iPSCs to identify those that both lack potentially harmful mutations and are readily differentiated into various cell types. In sum, the generation of iPSCs using a standardized process beginning from early progenitor cells isolated from routine blood draws minimizes this variation and is a good starting point to provide a more comparable baseline for analysis. We present the first steps towards a standardized process to make the generation of clinical-grade iPSCs a reality.

Materials and Methods

Ethics Statement

All human primary cells were generated in vitro from tissue samples from human donors with appropriate written informed consent given to the commercial providers.

All animal work was conducted according to relevant national and international guidelines under the approval of the Cellular Dynamics International Animal Care and Use Committee. As a private company, our animal facility does not provide a permit number or approval ID since mouse is not a protected species.

Processing whole blood samples

Peripheral (PB.1 and PB.2) and cord (CB.1 and CB.2) blood-derived CD34+ cells were obtained from AllCells (Emeryville, CA USA). Blood collections were performed at AllCells and Meriter Laboratories (Madison, WI USA) using standard, 8 ml Vacutainer Cell Processing Tubes (both sodium citrate and sodium heparin-based tubes are acceptable; BD Biosciences; Franklin Lakes, NJ USA). Appropriate documentation for informed consent was completed prior to blood collection (Meriter Laboratories). Vacutainers were processed within 24 hours of collection. Briefly, the PBMC-containing upper phase was collected and washed with ice-cold PBS (Invitrogen; Carlsbad, CA USA). Cells were either frozen down or used directly for purification with the CD34 MicroBead Kit (Miltenyi; Auburn, CA USA) and used according to the manufacturer’s protocol. Some samples were treated with Histopaque (Sigma Aldrich; St. Louis, MO USA) to minimize the number of red blood cells (RBCs) and centrifuged at 2000 rpm for 20 minutes without braking. The interface containing the PBMCs was removed if samples were treated with histopaque, cells washed again with chilled PBS, centrifuged at 600× g for 15 minutes and either frozen down with CryoStor10 (StemCell Technologies; Vancouver, BC Canada) or used directly for purification. CD34+ cell expansion media: StemSpan SFEM (StemCell Technologies), Flt3, SCF, TPO each at a final concentration of 300 ng/ml, IL-6 (100 ng/ml) and IL-3 (10 ng/ml) (Peprotech; Rocky Hill, NJ USA), supplemented with DNaseI (final concentration at 20 U/ml), and 1× Antibiotic-antimycotic (Invitrogen) for overnight recovery. Defined expansion media: serum-free StemSpan H3000 (StemCell Technologies), animal-free IL-6 (R&D Systems Minneapolis, MN USA), and recombinant human IL-3, TPO, Flt3, and SCF (Peprotech) at the same concentrations listed above. PBMC expansion media: StemSpan SFEM, ExCyte Medium Supplement (Millipore; Billerica, MA), Glutamax (Invitrogen), SCF (250 ng/ml), IL-3 (20 ng/ml), Erythropoietin (2 U/ml; Prospec; Rehovot, Israel), IGF-1 (40 ng/ml; Prospec), and Dexamethasone (1 µM; Fisher; Waltham, MA). PBMCs were resuspended at 1×106 cells/ml for expansion.

Flow cytometry

Cell surface staining of hematopoietic cells was performed with CD45-PE, CD34-APC, CD19-APC and CD56-PE (BD Biosciences) and CD3-PE (eBioscience; San Diego, CA USA) antibodies. iPSCs were processed directly for antibody staining for the presence of Tra-1-81 (Stemgent; Cambridge, MA USA) and SSEA-4 (BD Pharmingen; San Diego, CA USA). Propidium Iodide (Sigma Aldrich) was added for dead cell exclusion, and all stained cells were analyzed in combination with their respective isotype controls using a flow cytometer (Accuri; Ann Arbor, MI USA).

Reprogramming cells enriched for CD34-expression

The CD34 nucleofection kit and device (Lonza; Allendale, NJ USA) were used for transfections. For CD34+ cells, 3.5 µg of each plasmid in Combination Set 1 and 3 ug of each plasmid for Combination Set 2 except for the L-myc containing plasmid where 2 µg was transfected using program U-08. Cells were seeded onto RetroNectin-coated 6-well plates (Takara Bio, Inc; Otsu, Shiga Japan). Seeding density ranged from 5×104 to 1×105 cells/ml. Reprogramming media: StemSpan SFEM (StemCell Technologies) supplemented with non-essential amino acids (NEAA; Invitrogen), 0.5× Glutamax, N2B27 (Invitrogen), 0.1 mM β-mercaptoethanol (Sigma-Aldrich), 100 ng/mL zebrafish basic fibroblast growth factor (zbFGF), 0.5 µM PD0325901, 3 µM CHIR99021, 0.5 µM A-83-01 (all molecules from Stemgent), and 10 µM HA-100 (Santa Cruz; Santa Cruz CA USA). Conditions for PBMC reprogramming relied on 1×106 cells per transfection, program T-16, and DNA from Combination Set 2 at the concentrations described for CD34+ cells. Reprogramming media for PBMCs was the same with the exception of the small molecule cocktail which contained recombinant human LiF (Millipore), 3 uM CHIR99021, and 0.5 uM A-83-01. In general, cultures were fed with fresh medium every other day for 9 to 14 days then transitioned to TeSR2 (Stem Cell Technologies) without the addition of small molecules. iPSC colonies were scored with Tra-1-81 antibody (StainAlive™ DyLight™ 488 Mouse anti-Human Tra-1-81 antibody; Stemgent) or mouse-anti-Tra-1-60 IgM antibody (R&D) in combination with goat anti-mouse IgM Alexa 488 (Invitrogen), and alkaline phosphatase expression (Vector Blue Alkaline Phosphatase Substrate Kit III, Vector Laboratories; Burlingame, CA USA).

Detecting endogenous expression of pluripotency markers

Total RNA was isolated using the RNeasy Mini Plus kit (Qiagen; Valencia, CA USA) per the manufacturer’s protocol. Approximately 1 µg of total RNA was used for cDNA synthesis using the SuperScript III First-Strand Synthesis system for RT-PCR (Invitrogen). RT-PCR was performed using previously described primers and those listed in Table 6 [2]. cDNA was diluted 1:2 and 1 µl was used in reactions with GoTaq Green Master Mix (Promega; Madison, WI USA).

Table 6. Primer sequences for the detection of endogenous gene expression.

journal.pone.0027956.t006  Table 6. Primer sequences for the detection of endogenous gene expression.

Episomal and Genomic DNA isolation

Immunoglobulin heavy chain and T cell gene rearrangements


In Vitro Differentiation and Teratoma Studies


Author Contributions


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2. Yu J, Hu K, Smuga-Otto K, Tian S, Stewart R, et al. (2009) Human Induced Pluripotent Stem Cells Free of Vector and Transgene Sequences. Science.

3. Yu J, Vodyanik MA, Smuga-Otto K, Antosiewicz-Bourget J, Frane JL, et al. (2007) Induced pluripotent stem cell lines derived from human somatic cells. Science 318: 1917–1920. doi: 10.1126/science.1151526.

4. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, et al. (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131: 861–872. doi: 10.1016/j.cell.2007.11.019.

5. Okita K, Nakagawa M, Hyenjong H, Ichisaka T, Yamanaka S (2008) Generation of mouse induced pluripotent stem cells without viral vectors. Science 322: 949–953. doi: 10.1126/science.1164270.

6. Saha K, Jaenisch R (2009) Technical challenges in using human induced pluripotent stem cells to model disease. Cell Stem Cell 5: 584–595. doi: 10.1016/j.stem.2009.11.009.

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8. Yamanaka S (2010) Patient-specific pluripotent stem cells become even more accessible. Cell Stem Cell 7: 1–2. doi: 10.1016/j.stem.2010.06.009.

9. Brown ME, Rondon E, Rajesh D, Mack A, Lewis R, et al. (2010) Derivation of induced pluripotent stem cells from human peripheral blood T lymphocytes. PLoS One 5: e11373. doi: 10.1371/journal.pone.0011373.

10. Hanna J, Markoulaki S, Schorderet P, Carey BW, Beard C, et al. (2008) Direct reprogramming of terminally differentiated mature B lymphocytes to pluripotency. Cell 133: 250–264. doi: 10.1016/j.cell.2008.03.028.

11. Polo JM, Liu S, Figueroa ME, Kulalert W, Eminli S, et al. (2010) Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol 28: 848–855. doi: 10.1038/nbt.1667.

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15. Wada H, Kojo S, Kusama C, Okamoto N, Sato Y, et al. (2011) Successful differentiation to T cells, but unsuccessful B-cell generation, from B-cell-derived induced pluripotent stem cells. Int Immunol 23: 65–74. doi: 10.1093/intimm/dxq458.

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Analysis of Human and Mouse Reprogramming of Somatic Cells to Induced Pluripotent Stem Cells. What Is in the Plate?

Stéphanie Boué, Ida Paramonov,  María José Barrero,  Juan Carlos Izpisúa Belmonte
Published: September 17, 2010  http://dx.doi.org/10.1371/journal.pone.0012664

After the hope and controversy brought by embryonic stem cells two decades ago for regenerative medicine, a new turn has been taken in pluripotent cells research when, in 2006, Yamanaka’s group reported the reprogramming of fibroblasts to pluripotent cells with the transfection of only four transcription factors. Since then many researchers have managed to reprogram somatic cells from diverse origins into pluripotent cells, though the cellular and genetic consequences of reprogramming remain largely unknown. Furthermore, it is still unclear whether induced pluripotent stem cells (iPSCs) are truly functionally equivalent to embryonic stem cells (ESCs) and if they demonstrate the same differentiation potential as ESCs. There are a large number of reprogramming experiments published so far encompassing genome-wide transcriptional profiling of the cells of origin, the iPSCs and ESCs, which are used as standards of pluripotent cells and allow us to provide here an in-depth analysis of transcriptional profiles of human and mouse cells before and after reprogramming. When compared to ESCs, iPSCs, as expected, share a common pluripotency/self-renewal network. Perhaps more importantly, they also show differences in the expression of some genes. We concentrated our efforts on the study of bivalent domain-containing genes (in ESCs) which are not expressed in ESCs, as they are supposedly important for differentiation and should possess a poised status in pluripotent cells, i.e. be ready to but not yet be expressed. We studied each iPSC line separately to estimate the quality of the reprogramming and saw a correlation of the lowest number of such genes expressed in each respective iPSC line with the stringency of the pluripotency test achieved by the line. We propose that the study of expression of bivalent domain-containing genes, which are normally silenced in ESCs, gives a valuable indication of the quality of the iPSC line, and could be used to select the best iPSC lines out of a large number of lines generated in each reprogramming experiment.

Citation: Boué S, Paramonov I, Barrero MJ, Izpisúa Belmonte JC (2010) Analysis of Human and Mouse Reprogramming of Somatic Cells to Induced Pluripotent Stem Cells. What Is in the Plate? PLoS ONE 5(9): e12664.  http://dx.doi.org/10.1371/journal.pone.0012664

Figure 2. Human protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

journal.pone.0012664.g002  Figure 2. Human protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.


The human protein-protein interaction networks of genes most consistently highly expressed in ESCs and iPSCs, compared to the starting cell populations, have been created from the lists of the biggest changes in expression, using String[71] with high confidence interactions (min score 0.7) and have been edited in Medusa[72]. They show a central, highly interconnected network of genes in which the most famous pluripotency transcription factors are to be found and which is likely to represent the core pluripotency network. They also highlight a number of genes whose functions relate to cell-cell communication, cell cycle, DNA repair and other metabolisms.


Figure 3. Mouse protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

journal.pone.0012664.g003  Figure 3. Mouse protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

The mouse protein-protein interaction networks of genes most consistently highly expressed in ES and iPSCs, compared to the starting cell populations, have been created from the lists of biggest changes in expression, using String[71] with high confidence interactions (min score 0.7) and have been edited in Medusa[72]. They show a central, highly interconnected network of genes in which the most famous pluripotency transcription factors are to be found and which is likely to represent the core pluripotency network. They also highlight a number of genes those functions relate to cell-cell communication, cell cycle, DNA repair and other metabolisms.



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Reporter: Sudipta Saha, Ph.D.

Assessment of the propensity for vascular events has been based on measurement of risk factors predisposing one to vascular injury. These assessments are based on the strong associations between risk factors such as hypertension, cholesterol levels, smoking, and diabetes which were first described almost a half century ago. The more recent discovery of the relationship between ongoing inflammation and clinical outcomes has led to a variety of blood-based assays which may impart additional knowledge about an individual’s propensity for future cardiovascular events. Vascular health is now better represented as a balance between ongoing injury and resultant vascular repair, mediated at least in part by circulating endothelial progenitor cells (http://www.ncbi.nlm.nih.gov/pubmed/19124422). Accurate enumeration of circulating endothelial progenitor cells is essential for their potential application as biomarkers of angiogenesis. Different stem cell markers (CD34, CD133) and endothelial cell antigens (KDR/VEGFR-2, CD31) in different flow cytometric protocols are assessed for the purpose of circulating progenitor endothelial cell quantification (http://www.ncbi.nlm.nih.gov/pubmed/20381496). Enumeration of circulating progenitor endothelial cells are used in the assessment of various diseases and physiological states, such as: type 2 diabetes patients with peripheral vascular disease, certain phases during congestive heart failure, acute myocardial infarction, atherosclerosis, cardiovascular disease, physical training, cessation of smoking. Two modern instruments used now-a-days to measure the circulating progenitor endothelial cells are discussed below:

MACSQuant® Analyzer:

Circulating progenitor endothelial cells are defined by co-expression of the markers CD34, CD309 (VEGFR-2/KDR), and CD133, though CD133 expression is lost during maturation to endothelial cells.8-10 Since circulating progenitor endothelial cells are rare in peripheral blood, EPC enumeration protocols are rather extensive and laborious. To obtain reliable enumeration results for these rare cells, the sensitivity of flow cytometric analysis needs to be increased. This has been achieved by magnetic enrichment of circulating progenitor endothelial cells prior to flow cytometric analysis, which reduces the number of events that have to be analyzed. The circulating progenitor endothelial cell Enrichment and Enumeration Kit have been designed for enumeration of circulating progenitor endothelial cells from peripheral blood, cord blood, bone marrow, or leukapheresis products. In combination with magnetic pre-enrichment and flow cytometric analysis on the MACSQuant® Analyzer, this kit overcomes some of the limitations of circulating progenitor endothelial cell analysis and offers a simple and time effective solution for EPC enumeration. The circulating progenitor endothelial cell Enrichment and Enumeration Kit in combination with pre-enrichment and flow cytometric analysis on the MACSQuant Analyzer is an effective method to enumerate circulating progenitor endothelial cells in 10 mL of whole blood. Based on the calculated starting number of cells, the circulating progenitor endothelial cell Express Mode analysis template automatically calculates the absolute number and concentration of circulating progenitor endothelial cells in 10 mL of starting material, i.e., whole blood, bone marrow, cord blood, or leukapheresis products. The MACSQuant Analyzer has the ability to enrich cells using MACS technology. This capability makes the enumeration of circulating progenitor endothelial cells fast and easy. The entire process takes less than 2 hours to perform from blood draw to analyzed data and drastically reduces the time and difficulty of such a protocol by combining magnetic enrichment and flow cytometric analysis in one streamlined experiment (http://www.miltenyibiotec.com/downloads/6760/6764/18602/31184/MQ_ApplicationFlyer_EPC.pdf).

Attune® Acoustic Focusing Cytometer:

In cancer research, circulating progenitor endothelial cells have been suggested as a noninvasive biomarker for angiogenic activity, providing insight into tumor regrowth, resistance to chemotherapy, early recurrence, and metastasis during or after chemotherapy. In healthy individuals, circulating progenitor endothelial cells are reported to be present in very low numbers: 0.01%–0.0001% of all peripheral blood mononuclear cells. Flow cytometry offers the necessary collection and analysis capabilities for detection of circulating progenitor endothelial cells, but is subject to numerous technical challenges. In comparison to traditional hydrodynamic focusing cytometers, the Attune® Acoustic Focusing Cytometer, with its fast acquisition times and increased precision, overcomes the technological hurdles involved in analyzing circulating progenitor endothelial cells. The method includes a number of conventional ways to improve rare-event detection: a blocking step, a viability stain (SYTOX® AADvanced™ Dead Cell Stain), and the use of a dump channel to eliminate unwanted cells and decrease background fluorescence. The challenge of collecting a large enough number of events in a reasonable amount of time is met by using a collection rate of 1,000 μL/min with the Attune® cytometer. This setting enables the collection of more than 4,000,000 live white blood cell (WBC) events in just 35 minutes; the acquisition time using a traditional hydrodynamic focusing cytometer would be 10–12 times longer, close to 6 hours. Furthermore, this method delivers additional time savings by eliminating wash steps to avoid sample loss and employing a simpler sample preparation method. (http://zh.invitrogen.com/etc/medialib/files/Cell-Analysis/PDFs.Par.54318.File.tmp/CO24210-Human-CEC_cancer.pdf)

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