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Posts Tagged ‘myocardial infarct’


Progenitor Cell Transplant for MI and Cardiogenesis  (Part 1

Author and Curator: Larry H. Bernstein, MD, FCAP
and
Curator: Aviva Lev-Ari, PhD, RN
This article is Part I of a review of three perspectives on stem cell transplantation onto a substantial size of infarcted myocardium to generate cardiogenesis in tissue that is composed of both repair fibroblasts and cardiomyocytes, after essentially nontransmural myocardial infarct.

Progenitor Cell Transplant for MI and Cardiogenesis (Part 1)

Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/10/28/progenitor-cell-transplant-for-mi-and-cardiogenesis/

Source of Stem Cells to Ameliorate Damage Myocardium (Part 2)

Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013-10-29/larryhbern/Source_of_Stem_Cells_to_Ameliorate_ Damaged_Myocardium/

An Acellular 3-Dimensional Collagen Scaffold Induces Neo-angiogenesis
 (Part 3)

Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013-10-29/larryhbern/An_Acellular_3-Dimensional_Collagen_Scaffold _Induces_Neo-angiogenesis/

The same approach is considered for stroke in one of these studies.  These are issues that need to be considered
  1. Adult stem cells
  2. Umbilical cord tissue sourced cells
  3. Sheets of stem cells
  4. Available arterial supply at the margins
  5. Infarct diameter
  6. Depth of ischemic necrosis
  7. Distribution of stroke pressure
  8. Stroke volume
  9. Mean Arterial Pressure (MAP)
  10. Location of infarct
  11. Ratio of myocytes to fibrocytes
  12. Coexisting heart disease and, or
  13. Comorbidities predisposing to cardiovascular disease, hypertension
  14. Inflammatory reaction against the graft

Transplantation of cardiac progenitor cell sheet onto infarcted heart promotes cardiogenesis and improves function

L Zakharova1, D Mastroeni1, N Mutlu1, M Molina1, S Goldman2,3, E Diethrich4, and MA Gaballa1*
1Center for Cardiovascular Research, Banner Sun Health Research Institute, Sun City, AZ; 2Cardiology Section, Southern Arizona VA Health Care System, and 3Department of Internal Medicine, The University of Arizona, Tucson, AZ; and 4Arizona Heart Institute, Phoenix, AZ
Cardiovascular Research (2010) 87, 40–49   http://dx.doi.org/10.1093/cvr/cvq027

Abstract

Aims

Cell-based therapy for myocardial infarction (MI) holds great promise; however, the ideal cell type and delivery system have not been established. Obstacles in the field are the massive cell death after direct injection and the small percentage of surviving cells differentiating into cardiomyocytes. To overcome these challenges we designed a novel study to deliver cardiac progenitor cells as a cell sheet.

Methods and results

Cell sheets composed of rat or human cardiac progenitor cells (cardiospheres), and cardiac stromal cells were transplanted onto the infarcted myocardium after coronary artery ligation in rats. Three weeks later, transplanted cells survived, proliferated, and differentiated into cardiomyocytes (14.6 ± 4.7%). Cell sheet transplantation suppressed cardiac wall thinning and increased capillary density (194 ± 20 vs. 97 ± 24 per mm2, P < 0.05) compared with the untreated MI. Cell migration from the sheet was observed along the necrotic trails within the infarcted area. The migrated cells were located in the vicinity of stromal-derived factor (SDF-1) released from the injured myocardium, and about 20% of these cells expressed CXCR4, suggesting that the SDF-1/CXCR4 axis plays, at least, a role in cell migration. Transplantation of cell sheets resulted in a preservation of cardiac contractile function after MI, as was shown by a greater ejection fraction and lower left ventricular end diastolic pressure compared with untreated MI.

Conclusion

The scaffold-free cardiosphere-derived cell sheet approach seeks to efficiently deliver cells and increase cell survival.These transplanted cells effectively rescue myocardium function after infarction by promoting not only neovascular-ization but also inducing a significant level of cardiomyogenesis
Keywords  Myocardial infarction • Cardiac progenitor cells • Cardiospheres • Cardiac regeneration • Contractility

Introduction

Despite advances in cardiac treatment after myocardial infarction (MI), congestive heart failure remains the number one killer world-wide. MI results in an irreversible loss of functional cardiomyocytes followed by scar tissue formation. To date, heart transplant remains the gold standard for treatment of end-stage heart failure, a procedure which will always be limited by the availability of a donor heart. Hence, developing a new form of therapy is vital.
A number of adult non-cardiac progenitor cells have been tested for myocardial regeneration, including skeletal myoblasts,1 bone-marrow2, and endothelial progenitor cells.3,4 In addition, several cardiac resident stem cell populations have been characterized based on the expression of stem cell marker proteins.5–8 Among these, the c-Kit+ population has been reported to promote myocardial repair.5,9 Recently, an ex vivo method to expand cardiac-derived progenitor cells from human myocardial biopsies and murine hearts was developed.10 Using this approach, undifferentiated cells (or cardiospheres) grow as self-adherent clusters from postnatal atrium or ventricular biopsy specimens.11
To date, the most common technique for cell delivery is direct injection into the infarcted myocardium.12 This approach is inefficient because more than 90% of the delivered cells die by apoptosis and only a small number of the survived cells differentiated into cardiomyocytes.13 An alternative approach to cell delivery is a biodegradable scaffold-based engineered tissue.14,15 This approach has the clear advantage in creating tissue patches of different shapes and sizes and in creating a beating heart by decellularization technology.16 Advances are being made to overcome the issue of small patch thickness and to minimize possible toxicity of the degraded substances from the scaffold.15 Recently, scaffold-free cell sheets were created from fibroblasts, mesenchymal cells, or neonatal myocytes.17,18 Transplantation of these sheets resulted in a limited improvement in cardiac function due to induced neovascularization and angiogenesis through secretion of angiogenic factors.17–19 However, few of those progenitor cells have differentiated into cardiomyocytes.17 The need to improve cardiac contractile function suggests focusing on cells with higher potential to differentiate to cardiomyocytes with an improved delivery method.
In the present study, we report a cell-based therapeutic strategy that surpasses limitation inherent in previously used methodologies. We have created a scaffold-free sheet composed of cardiac progenitor cells (cardiospheres) incorporated into a layer of cardiac stromal cells. The progenitor cells survived when transplanted as a cell sheet onto the infarcted area, improved cardiac contractile functions, and supported recovery of damaged myocardium by promoting not only vascularization but also a significant level of cardiomyogenesis. We also showed that cells from a sheet can be recruited to the site of injury driven, at least partially, by the stromal-derived factor (SDF-1) gradient.

Methods

Detailed methods are provided in the Supplementary Methods

Animals

Three-month-old Sprague Dawley male rats were used. Rats were randomly placed into four groups:
(1) sham-operated rats, n = 12;
(2) MI, n = 12;
(3) MI treated with rat sheet, n = 10; and
(4) MI treated with human sheet, n = 10.

Myocardial infarction

MI was created by the ligation of the left coronary artery.20 Animals were intubated and ventilated using a small animal ventilator (Harvard Apparatus). A left thoracotomy was performed via the third intercostal rib, and the left coronary artery was ligated. The extent of infarct was verified by measuring the area at risk: heart was perfused with PBS containing 4 mg/mL Evans Blue as previously described by our laboratory.20 The area at risk was estimated by recording the size of the under-perfused (pale-colored) area of myocardium (see Supplementary material online, Figure S1). Only animals with an area at risk >30% were used in the present study. Post-mortem infarct size was measured using triphenyl tetrazolium chloride staining as previously described by our laboratory.20

Isolation of cardiosphere-forming cells

Cardiospheres were generated as described10 from atrial tissues obtained from:
(1) human atrial resection samples obtained from patients (aged from 53 to 73 years old) undergoing cardiac bypass surgery at Arizonam Heart Hospital (Phoenix, AZ) in compliance with Institutional Review Board protocol (n = 10),
(2) 3-month-old SD rats (n = 10). Briefly, tissues were cut into 1–2 mm3 pieces and tissue fragments were cultured ‘as explants’ in a complete explants medium for 4 weeks (Supplementary Methods).
Cell sheet preparation, labelling, handling, and transplantation
Cardiosphere-forming cells (CFCs) combined with cardiac stromal cells were seeded on double-coated plates (poly-L-lysine and collagen type IV from human placenta) in cardiosphere growing medium (Supplementary Methods). The sheets created from the same cell donors were divided into two groups,
one for transplantation and the other for characterization by immunostaining and RT–PCR (Supplementary Methods).
Prior to transplantation, rat cell sheets were labelled with 2 mM 1,1-dioctadecyl-3,3,3,3-tetramethylindocarbocyanine, DiI, for tracking transplanted cells in rat host myocardium (Molecular Probes, Eugene, OR). Sheets created using human cells were transplanted unlabelled. Sheets were gently peeled off the collagen-coated plate and folded twice to form four layers. The entire sheet with 200 ml of media was
  • gently aspirated into the pipette tip,
  • transferred to the supporting polycarbonate filter (Costar) and
  • spread off by adding media drops on the sheet (Figure 2A).
Polycarbonate filter was used as a flexible mechanical support for cell sheet to facilitate handling during the transplantation. Immediately after LAD occlusion, the cell sheet was transplanted onto the infarcted area, allowed to adhere to the ventricle for 5–7 min, and the filter was removed before closing the chest (Figure 2A).

Cardiac function

Three weeks after MI, closed-chest in vivo cardiac function was measured using a Millar pressure conductance catheter system (Millar Instruments, Houston, TX) (Supplementary Methods).

Cell sheet survival, engraftment, and cell migration

Rat host myocardium and cell sheet composition after transplantation were characterized by immunostaining (Supplementary Methods). Rat-originated cells were traced by DiI, while human-originated cells were identified by immunostaining with anti-human nuclei or human lamin antibodies.
  1. To assess sheet-originated cardiomyocytes within the host myocardium, the number of cells positive for both human nuclei and myosin heavy chain (MHC) (human sheet); or both DiI and MHC (rat sheet) were counted.
  2. To assess sheet-originated capillaries within the rat host myocardium, the number of cells positive for both human nuclei and von Willebrand factor (vWf) (human sheet); or both DiI and vWf (rat sheet) were counted. Cells were counted in five microscopic fields within cell sheet and area of infarct (n = 5). The number of cells expressing specific markers was normalized to the total number of cells determined by 40,6-diamidino-2-phenylindole staining of the nuclei DNA.
  3. To assess the survival of transplanted cells, sections were stained with Ki-67 antibody followed by fluorescent detection and caspase 3 primary antibodies followed by DAB detection (Supplementary Methods).
  4. To evaluate human sheet engraftment, sections were stained with human lamin antibody followed by fluorescent detection (Supplementary Methods).
  5. Rat host inflammatory response to the transplanted human cell sheet 21 days after transplantation was evaluated by counting tissue mononuclear phagocytes and neutrophils (Supplementary Methods).

Imaging

Images were captured using Olympus IX70 confocal microscope (Olympus Corp, Tokyo, Japan) equipped with argon and krypton lasers or Olympus IX-51 epifluorescence microscope using excitation/emission maximum filters: 490/520 nm, 570 /595 nm, and 355 /465 nm. Images were processed using DP2-BSW software (Olympus Corp).

Statistics

All data are represented as mean ± SE Significance (P < 0.05) was deter-mined using ANOVA (StatView).

Results

Generation of cardiospheres

Cardiospheres were generated from atrial tissue explants. After 7–14 days in culture, a layer of stromal cells arose from the attached explants (Supplementary material online, Figure S2a). CFCs, small phase-bright single cells, emerged from explants and bedded down on the stromal cell layer (Supplementary material online, Figure S2b).
  • After 4 weeks, single CFCs, as well as cardiospheres (spherical colonies generated from CFCs) were observed (Supplementary material online, Figure S2c).
Cellular characteristics of cardiospheres in vitro
Immunocytochemical analysis of dissociated cardiospheres revealed that
  • 30% of cells were c-Kitþ indicating that the CFCs maintain multi-potency. About
  • 22 and 28% of cells expressed a, b-MHC and cardiac troponin I, respectively.
These cells represent an immature cardiomyocyte population because they were smaller (10–15 pm in length vs. 60–80 pm for mature cardiomyocytes) and no organized structure of MHC was detected. Furthermore
  • 17% of the cells expressed a-smooth muscle actin (SMA) and
  • 6% were positive for vimentin,
    • both are mesenchymal cell markers (Supplementary material online, Figure S3a and b).
  • Less then 5% of cells were positive for endothelial cell marker; vWf.
Cell characteristics of human cardiospheres are similar to those from rat tissues (Supplementary material online, Figure S3c).
Cardiospheres were further characterized based on the expression of c-Kit antigen. RT–PCR analysis was performed on both c-Kitþ and c-Kit2 subsets isolated from re-suspended cardiospheres. KDR, kinase domain protein receptor, was recently identified as a marker for cardiovascular lineage progenitors in differentiating embryonic stem cells.21 Here, we found that
  • the c-Kitþ cells were also Nkx2.5 and GATA4-positive, but were low or negative for KDR (Supplementary material online, Figure S3d). In contrast,
  • c-Kit2 cells strongly expressed KDR and GATA4, but were negative for Nkx2.5.
  • Both c-Kitþ and c-Kit2 subsets did not express Isl1, a marker for multipotent secondary heart field progenitors.22
Characteristics of cell sheet prior to transplantation
The cell sheet is a layer of cardiac stromal cells in which the cardiospheres were incorporated at a frequency of 21 ± 0.5 spheres per 100,000 viable cells (Figure 1A). The average diameter of cardiospheres within a sheet was 0.13 ± 0.02 mm and their average area was 0.2 ± 0.06 mm2 (Figure 1A). After sheets were peeled off the plate, it exhibited a heterogeneous thickness ranging from 0.05– 0.1 mm (n 1/4 10), H&E staining (Figure1B) and Masson’s Trichrome staining (Figure 1C) of the sheet sections revealed tissue-like organized structures composed of muscle tissue intertwined with streaks of collagen with no necrotic core. Based on the immunostaining results, sheet compiled of several cell types including
  • SMAþ cardiac stromal cells (50%),
  • MHCþ cardiomyocytes (20%), and
  • vWfþ endothelial cells (10%) (Figure 1D and E).
  • 15% of the sheet-forming cells were c-Kitþ suggesting the cells multipotency (Figure 1E).
  • Cells within the sheet expressed gap-junction protein C43, an indicator of electromechanical coupling between cells (Figure 1D).
  • 40% of cells were positive for the proliferation marker Ki-67 suggesting an active cell cycle state (Figure 1D, middle panel).
Human sheet expressed genes
  1. known to be upregulated in undifferentiated cardiovascular progenitors such as c-Kit and KDR;
  2. cardiac transcription factors Nkx2.5 and GATA4; genes related to adhesion, cell homing, and
  3. migration such as ICAM (intercellular adhesion molecule), CXCR4 (receptor for SDF-1), and
  4. matrix metalloprotease 2 (MMP2).
No expression of Isl1 was detected in human sheet (Figure 1F).
sheet transplant on MI_Image_2
Figure 1 Cell sheet characteristics. (A) Fully formed cell sheet. Arrow indicates integrated cardiosphere. (B) H&E staining; pink colour (arrowhead) indicates cytosol and blue (arrows) indicates nuclear stain. Note that there is no necrotic core within the cell sheet. (C) Masson’s Trichrome staining of sheet section. Arrowhead indicates collagen deposition within the sheet. (D and E) Sheet sections were labelled with antibodies against following markers: (D) vWf (green), Ki-67 (green), C43 (green); (E) c-Kit (green), MHC (red), SMA (red) as indicated on top of each panel. Nuclei were labelled with blue fluorescence of 40,6-diamidino-2-phenylindole (DAPI). (F) Gene expression analysis of the cell sheet. Scale bars, 200 pm (A) or 50 pm (B–E).

Cell sheet survival and proliferation

Two approaches were used to track transplanted cells in the host myocardium.
  • rat cell sheets were labelled with red fluorescent dye, DiI, prior to the transplantation.
  • the sheet created from human cells (human sheet) were identified in rat host myocardium by immunostaining with human nuclei antibodies.
DiI-labelling together with trichrome staining showed engraftment of the cardiosphere-derived cell sheet to the infarcted myocardium (Figure 2B–D). In vivo sheets grew into a stratum with heterogeneous thickness ranging from 0.1–0.5 mm over native tissue. The percentage of Ki-67þ cells within the sheet was 37.5 ± 6.5 (Figure 2F) whereas host tissue was mostly negative (except for the vasculature).
To assess the viability of transplanted cells, the heart sections were stained with the apoptosis marker, caspase 3. A low level of caspase 3 was detected within the sheet, suggesting that the majority of transplanted cells survived after transplantation (Figure 2G).
sheet transplant on MI_Image_3
Figure 2 Transplantation and growth of cell sheet after transplantation.
(A) Sheet transplantation onto infarcted heart. Detached cell sheet on six-well plate (left); cell sheet folded on filter (middle); and transplanted onto left ventricle (right). Scale bar 2 mm. DiI-labelled cell sheets grafted above MI area at day 3
(B) and day 21
(C) after transplantation.
(D) LV section of untreated MI rat at day 21 showing no significant red fluorescence background.
Bottom row (B–D) demonstrates the enlargement of box-selected area of corresponding top panels.
(E) Similar sections stained with Masson’s Trichrome. Section of rat (F) or human (G) sheet treated rat at day 21 after MI.
(F) Section was stained with antibody against Ki-67 (green). Cell sheet was pre-labelled with DiI (red). Nuclei stained with blue fluorescence of DAPI.
(G) Section was double stained with human nuclei (blue) and caspase 3 (brown, arrows) antibodies and counterstained with eosin.
Asterisks (**) indicate cell sheet area. Scale bars 200 mm (B–D, top row), 100 mm (B–D, bottom row, and E) or 50 mm (F, G).
Identification of inflammatory response
Twenty-one days after transplantation of human cell sheet, inflammatory response of rat host was examined. Transplantation of human sheet on infarcted rats reduced the number of mononuclear phagocytes (ED1-like positive cells) compared with untreated MI control (Supplementary material online, Figure S4a–e and l). In addition, the number of neutrophils was similar in both control untreated MI and sheet-treated sections (Supplementary material online, Figure S4f–k and m). These data suggest that at 21 days post transplantation, human cell sheet was not associated with significant infiltration of host immune cells.

Cell sheet engraftment and migration

Development of new vasculature was determined in cardiac tissue sections by co-localization of DiI labelling and vWf staining (Figure 3C). Three weeks after transplantation, the capillary density of ischaemic myocardium in the sheet-treated group significantly increased compared with MI animals (194 ± 20 vs. 97 ± 24 per mm2, P < 0.05, Figure 3A and B). The capillaries originated from the sheet ranged in diameter from 10 to 40 jim (n 1/4 30). A gradient in capillary density was observed with higher density in the sheet area which was decreased towards underlying infarcted myocardium. Mature blood vessels were identified within the sheet area and in the underlying myocardium in close proximity to the sheet evident by vWf and SMA double staining (Figure 3D).
sheet transplant on MI_Image_4
Figure 3 Neovascularization of infarcted wall. (A) Frozen tissue sections stained with vWf antibody (green). LV section of control (sham), infarcted (MI), and MI treated with cell sheet (sheet) rats. Scale bar, 100 jim. (B) Capillary density decreased in the MI compared with sham (*P < 0.05) and improved after cell sheet treatment (#P < 0.05). (C) Neovascularization within cell sheet area was recognized by co-localization of DiI- (red) and vWf (green) staining. Scale bar 100 jim. (D) Mature blood vessels (arrows) were identified by co-localization of SMA (red) and vWf (green) staining. Scale bar 50 jim.
Furthermore, 3 weeks after transplantation, a large number of labelled human nuclei positive or DiI-labelled cells were detected deep within the infarcted area indicating cell migration from the epicardial surface to the infarct (Figure 4A, B, and D). Minor or no migration was detected when the cell sheet was transplanted onto non-infarcted myocardium, sham control (Figure 4C). To evaluate engraftment of sheet-originated cells, sections were labelled with anti-human nuclear lamin antibody. Quantification of engraftment was performed using two approaches: fluorescence intensity and cell counting. Fluorescence intensity of the signal was analysed and compared for different areas of myocardium (Figure 4E–J). Since the transplanted sheets are created by human cells and are stained with human nuclear lamin-labelled with green fluorescence, the signal intensity of the sheet is set to 100% (100% of cells are lamin-positive). Myocardial area with no or limited number of labelled cells had the lowest level of fluorescence signal (13%, or 3.2 ± 1.4% of total number of cells), while
  1. the area where the cell migrated from the sheet to the infarcted myocardium had higher signal intensity (47%, or 11.9 ± 1.7% of total number of cells), indicating a higher number of sheet-originated cells are engrafted in the infarcted area.) (Figure 4K and L).
  2. Migrated cells were positive for KDR (Supplementary material online, Figure S5).
sheet transplant on MI_Image_5
Figure 4 Engraftment quantification of cells migrated from the sheet into the infarcted area of MI. Animals were treated with rat (A) or human (B–F) sheets. Cardiomyocytes were labelled with MHC antibody (A, green or B, red). Rat sheet-originated cells were identified with DiI-labelling, red (A). Arrows indicate the track of migrating cells. Human sheet-originated cells were identified by immunostaining with human nuclei antibody followed by secondary antibodies conjugated with either Alexa 488 (B, E and F, green) or AP (C, D, blue). No migration was detected when the cell sheet was transplanted onto non-infarcted myocardium (C). Heart sections were counterstained with eosin, pink (C–D). Higher magnification of area selected in the box is presented (D, right). Immunofluorescence of sheet (green) grafted to the myocardium surface (E) or cells migrated to the infarction area (F). Fluorescence profiles acrossthe cell sheet itself(G, box 1), area underlying cell sheet (I, box 2) and infarction areawith migrated cells (F, box 3). Mean fluorescence intensityofthe grafted human (K) cells was determined by outlining the region of interest (ROI) and subtracting the background fluorescence for the same region. Fluorescence intensity was normalized to the area of ROI (ii 1/4 6). (L) Percent engraftment was defined as number of lamin-positive cells divided by total number of cells per ROI. ‘M’, myocardium,’S’ sheet, ‘I’ infarction. Scale bars 100 mm (A–C, D, left, E and F), or 50 mm (D, right).
To elucidate a possible mechanism of cell migration, sections were stained to detect SDF1 and its unique receptor CXCR4. The migration patterns of cells from the sheet coincided with SDF-1 expression. Within 3 days after MI, SDF-1 was expressed in the injured myocardium (Figure 5A). At 3 weeks after MI and sheet transplantation, SDF-1 was co-localized with the migrated labelled cells (Figure 5B). PCR analysis revealed CXCR4 expression in cell sheet before transplantation (Figure 1F). However, after transplantation only a fraction of migrated cells expressed CXCR4 (Figure 5C).
sheet transplant on MI_Image_6
Figure 5 Migration of sheet-originated cells into the infarcted area. Confocal images of MI animals treated with sheets from rats (A and B) or human (C). SDF1 (green) was detected at border zone of the infarct at day 3 (A) and day 21 (B). Rat sheet-originated cells were identified with DiI-labelling (red). Note co-localization of DiI-positive sheet-originated cells with SDF1 at 21 days after MI (B). Human cells were identified by immunostaining with human nuclei antibody, red, (C). Note human cells that migrated to the area of infarct express CXCR4 (green) (C). Scale bar, 200 mm (A, B) or 50 mm (C). ‘M’, myocardium, ‘S’ sheet, ‘I’ infarct.

3.7 Cardiac regeneration

The differentiation of migrating cells into cardiomyocytes was evident by the co-localization of MHC staining with either human nuclei (Figure 6A) or DiI (Figure 6B and C). In contrast to the immature cardiomyocyte-like cells within the pre-transplanted cell sheet, the migrated and newly differentiated cells within the myocardium were about 30–50 mm in size and co-expressed C43 (see Supplementary material online, Figure S6). Cardiomyogenesis within the infarcted myocardium was observed in the sheets created from either rat or human cells.
sheet transplant on MI_Image_6
Figure 6 Cardiac regeneration. Sections of MI animals treated with human (A) or rat (B, C) sheets. Human sheet was identified by immunostaining with human nuclei antibody (green). Section was double-stained with MHC (red) antibody. Newly formed cardiomyocytes was identified by co-localization of human nuclei and MHC (yellow, arrow). (B) Rat sheet-originated cells were identified by DiI labelling (red). Section was double-stained with MHC (green) antibody. Newly formed cardiomyocytes were detected by co-localization of DiI with MHC (yellow, arrows). (C) Higher magnification of area selected in the boxes (B). Scale bars 200 mm (B), or 20 mm (A, C). ‘M’, myocardium, ‘S’ sheet, ‘I’ infarct.

Cell sheet improved cardiac contractile function and retarded LV remodelling after MI

Closed-chest in vivo cardiac function was derived from left ventricle (LV) pressure–volume loops (PV loops), which were measured using a solid-state Millar conductance catheter system. MI resulted in a characteristic decline in LV systolic parameters and an increase in diastolic parameters (Table 1). Cell sheet treatment improved both systolic and diastolic parameters (Table 1). Specifically, load-dependent parameters of systolic function: ejection fraction (EF), dP/dTmax, and cardiac index (CI) were decreased in MI rats and increased towards sham control with the cell sheet treatment (Table 1). Diastolic function parameters, dP/dTmin, relaxation constant (Tau), EDV, and EDP were increased in the MI rats and returned towards sham control parameters after sheet treatment (Table 1). However, load-independent systolic function, Emax, was decreased after MI. Treatment with human sheet improved Emax, while treatment with rat sheet had no effect (Table 1). Treatment with either rat or human sheets retarded LV remodelling; as such that it increased the ratio of anteriolateral wall thickness/LV inner diameter (t/Di) and wall thickness/LV outer diameter (t/Do) (see Supplementary material online, Table S3). However, human sheets appear to further improve LV remodelling compared with rat sheets as indicated by increased ratio of wall thickness to ventricular diameter and decreased both EDV and EDP (Table 1 and see Supplementary material online, Table S3).
Table 1 Hemodynamic parameters
Table 1. hemodynamic parameters

Discussion

The majority of the cardiac progenitor cells delivered using our scaffold-free cell sheet survived after transplantation onto the infarcted heart. A significant percentage of transplanted cells migrated from the cell sheet to the site of infarction and differentiated into car-diomyocytes and vasculature leading to improving cardiac contractile function and retarding LV remodelling. Thus, delivery of cardiac progenitor cells together with cardiac mesenchymal cells in a form of scaffold-free cell sheet is an effective approach for cardiac regeneration after MI.
Consistent with previous studies,5,11 here we showed that cardio-spheres are composed of multipotent precursors, which have the capacity to differentiate to cardiomyocytes and other cardiac cell types. When we fractioned cardiospheres based on c-Kit expression, we identified two subsets: Kitþ /KDR2/low/Nkx2.5þ and Kit2/KDRþ/ Nkx2.52(Supplementary material online, Figure S3d), which are likely reflecting cardiac and vascular progenitors.20
In the present study, delivery of cardiac progenitor cells as a cell sheet facilitates cell survival after transplantation. Necrotic cores, commonly observed in tissue engineered patches,23,24 are absent in cardiosphere sheets prior to transplantation (Figure 1B and C). Poor cell survival is caused by multiple processes such as: ischemia from the lack of vasculature and anoikis due to cell detachment from sub-strate.25 A possible mechanism of cell survival within the sheet is the induction of neo-vessels soon after transplantation due to the presence of endothelial cells within the sheet before transplantation (Figure 10). The cell sheet continued to grow in vivo (Figure 2B and C), suppressed cardiac wall thinning, and prevented LV remodelling at 21 days after transplantation (see Supplementary material online, Table S3). This maybe due to the induction of neovascularization (Figure 3), which may prevents ischemia-induced cell death (Figure 2G). Another likely mechanism of cell survival is that the cells within the scaffold-free sheet maintained cell-to-cell adhesion16 as shown by ICAM expression (Figure 1F). The cells also exhibit C43-positive junctions (Figure 10, see Supplementary material online, Figure S6), which may facilitate electromechanical coupling between the transplanted cells and the native myocardium.
We observed cell migration from the sheet to the infarcted myocardium (Figure 4A and B, E and F), which may be facilitated by the strong expression of MMP2 in the cell sheet (Figure 1F). Although, the mechanism of cardiac progenitor cell migration remains unclear, previous observations showed that SDF-1 is upregulated after MI and plays a role in bone-marrow and cardiac stem cell migration.26,27 Our data suggest that SDF-1-CXCR4 axis plays, at least in part, a role in cardiac progenitor cell migration from cell sheet to the infarcted myocardium. This conclusion is based on the following observations: (1) cell sheet expresses CXCR4 prior to transplantation (Figure 1F), (2) migrated cells are located in the vicinity of SDF-1 release (Figure 5A and B), and (3) about 20% of migrated cells expressed CXCR4. Note, not all the migrated cells expressed CXCR4 suggesting other mechanisms are involved in cell migration (Figure 5C).
Here we report that implanting cardiosphere-generated cell sheet onto infarcted myocardium not only improved vascularization but also promoted cardiogenesis within the infarcted area (Figure 6). A larger number of newly formed cardiomyocytes were found deep within the infarct compared with the cell sheet periphery. Notably the transplantation of the cell sheet resulted in a significant improvement of the cardiac contractile function after MI, as was shown by an increase of EF and decrease of LV end diastolic pressure (Table 1).
The beneficial effect of cell sheet is, in part, due to the presence of a large number of activated cardiac mesenchymal stromal cells (myofibroblasts) within the sheet. Myofibroblasts are known to provide a mechanical support for grafted cells, facilitating contraction28 and to induce neovascularization through the release of cytokines.17 In addition, mesenchymal cells are uniquely immunotolerant. In xenograft models unmatched mesenchymal cells transplanted to the heart of immunocompetent rats were shown to suppress host immune response29 presumably due to inhibition of T-cell activation.30 Consistently with previous study from our laboratory,31 here, we demonstrated host tolerance to the cell sheet 21 days after MI. Finally, phase II and III clinical trials are currently undergoing in which allogeneic MSCs are used to treat MI in patients (Osiris Therapeutic, Inc.).
In summary, our results show that cardiac progenitor cells can be delivered as a cell sheet, composed of a layer of cardiac stromal cells impregnated with cardiospheres. After transplantation, cells from the cell sheet migrated to the infarct, partially driven by SDF-1 gradient, and differentiated into cardiomyocytes and vasculature. Transplantation of cell sheet was associated with prevention of LV remodelling, reconstitution of cardiac mass, reversal of wall thinning, and significant improvement in cardiac contractile function after MI. Our data also suggest that strategies, which utilize undigested cells, intact cell–cell interactions, and combined cell types such as our scaffold-free cell sheet should be considered in designing effective cell therapy.

References

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Dawn B, Stein AB, Urbanek K, Rota M, Whang B, Rastaldo R et al. Cardiac stem cells delivered intravascularly traverse the vessel barrier, regenerate infarcted myocardium, and improve cardiac function. Proc Natl Acad Sci USA 2005;102:3766–3771.

 

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Scale‑Free Diagnosis of AMI from Clinical Laboratory Values

William P. Fisher, Jr., Larry H. Bernstein, Thomas A Naegele, Arden

Forrey, Asadullah Qamar, Joseph Babb, Eugene W. Rypka, Donna Yasick

Objective. Clinicians are often challenged with interpreting myriads of laboratory test results with few resources for knowing which values are most relevant, when any given value indicates a need for action, or how urgent the need for action is. The arrival of the electronic health record creates a context in which computational resources for meeting these challenges will be readily available. The purpose of this study was to evaluate the feasibility of employing probabilistic conjoint (Rasch) measurement models for creating the needed scale‑free standard measures and data quality standards.

Methods. Pathology data from 144 clients suspected of suffering myocardial infarctions were obtained. Thirty indicators were converted from their original values to ratings indicating a worsening of condition. These conversions took advantage of the fact that serial measurement of creatine kinase (CK; EC 2.7.3.2) isoenzyme MB (CK‑MB) and lactic dehydrogenase (LD; EC 1.1.1.27) isoenzyme 1 (LD‑1) in serum have characteristic evolutions in acute myocardial infarction (AMI). CK‑MB concentration begins to rise within 4 to 8 hours, peaks at 12 to 24 hours, and returns to normal within 48 to 72 hours. LD‑1 becomes elevated as early as 8 to 24 hours after infarction, and reaches a peak in 48 to 72 hours. However, the ratio of serum activity of LD‑1/total LD may be more definitive than LD‑1 activity itself. While these are most important in ECG negative AMI, they are not by themselves a “gold standard” for diagnosis.

The additional information and functionality required for such standards, including probabilistic estimates of scale parameters whose values do not depend on the calibrating sample and the capacity to deal with missing data, were sought by fitting the data to a Rasch partial credit model. This model estimates separate rating step values for each group of items sharing a common rating structure, en route to testing the hypothesis that the items work together to delineate a unidimensional measurement continuum defined by the repetition of a single unit quantity.

Results. Twenty of the 30 items were identified as delineating a unidimensional continuum.  Client measurement reliability was 0.90, and item calibration reliability was 0.96. Overall model fit is indicated by the client information‑ weighted mean square fit (infit) statistic (mean = .94, SD = .34) and  outlier‑ sensitive mean square fit (outfit) statistic (mean = 1.02, SD = .72), and the item infit (mean = .99, SD = .41) and outfit (mean = 1.04, SD = .72). The data‑to‑ model global fit is also indicated by the chi‑square of 3094.5, with 164 maximum independent parameters, 2766 maximum degrees of  freedom, and a probability (statistical significance) of less than .01 that this ora greater chi‑square would be observed with perfect data‑model fit.

Discussion. The analysis identified the 20 values most relevant to the diagnosis of AMI; these data may also support the construction of a unidimensional measure of AMI severity. If the construct supports both diagnostic and severity inferences, then the clinical action needed and its urgency will be indicated by the client’s measure. Similar analyses of data from other diagnostic groups will determine the extent to which lab value item relevance and hierarchies vary across diagnoses; such variation will be crucial to determining computer‑based decision support algorithms, which will match individual clients’ data with specific diagnostic profiles. Further analyses will also demonstrate the extent to which diagnosis is affected by missing data.

 

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Demonstration of a diagnostic clinical laboratory neural network agent applied to three laboratory data conditioning problems

Izaak Mayzlin                                                                        Larry Bernstein, MD

Principal Scientist, MayNet                                            Technical Director

Boston, MA                                                                          Methodist Hospital Laboratory, Brooklyn, NY

Our clinical chemistry section services a hospital emergency room seeing 15,000 patients with chest pain annually.  We have used a neural network agent, MayNet, for data conditioning.  Three applications are – troponin, CKMB, EKG for chest pain; B-type natriuretic peptide (BNP), EKG for congestive heart failure (CHF); and red cell count (RBC), mean corpuscular volume (MCV), hemoglobin A2 (Hgb A2) for beta thalassemia.  Three data sets have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach.  Optimum decision points for classifying using these data were determined using ROC (SYSTAT, 11.0), LCM (Latent Gold), and ordinal regression (GOLDminer).   The ACS and CHF studies both had over 700 patients, and had a different validation sample than the initial exploratory population.  The MayNet incorporates prior clustering, and sample extraction features in its application.   Maynet results are in agreement with the other methods.

Introduction: A clinical laboratory servicing a hospital with an  emergency room seeing 15,000 patients with chest pain to produce over 2 million quality controlled chemistry accessions annually.  We have used a neural network agent, MayNet, to tackle the quality control of the information product.  The agent combines a statistical tool that first performs clustering of input variables by Euclidean distances in multi-dimensional space. The clusters are trained on output variables by the artificial neural network performing non-linear discrimination on clusters’ averages.  In applying this new agent system to diagnosis of acute myocardial infarction (AMI) we demonstrated that at an optimum clustering distance the number of classes is minimized with efficient training on the neural network. The software agent also performs a random partitioning of the patients’ data into training and testing sets, one time neural network training, and an accuracy estimate on the testing data set. Three examples to illustrate this are – troponin, CKMB, EKG for acute coronary syndrome (ACS); B-type natriuretic peptide (BNP), EKG for the estimation of ejection fraction in congestive heart failure (CHF); and red cell count (RBC), mean corpuscular volume (MCV), hemoglobin A2 (Hgb A2) for identifying beta thalassemia.  We use three data sets that have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach.

In previous studies1,2 CK-MB and LD1 sampled at 12 and 18 hours postadmission were near-optimum times used to form a classification by the analysis of information in the data set. The population consisted of 101 patients with and 41 patients without AMI based on review of the medical records, clinical presentation, electrocardiography, serial enzyme and isoenzyme  assays, and other tests. The clinical or EKG data, and other enzymes or sampling times were not used to form a classification but could be handled by the program developed. All diagnoses were established by cardiologist review. An important methodological problem is the assignment of a correct diagnosis by a “gold standard” that is independent of the method being tested so that the method tested can be suitably validated. This solution is not satisfactory in the case of myocardial infarction because of the dependence of diagnosis on a constellation of observations with different sensitivities and specificities. We have argued that the accuracy of diagnosis is  associated with the classes formed by combined features and has greatest uncertainty associated with a single measure.

Methods:  Neural network analysis is by MayNet, developed by one of the authors.  Optimum decision points for classifying using these data were determined using ROC (SYSTAT, 11.0), LCM (Latent Gold)3, and ordinal regression (GOLDminer)4.   Validation of the ACS and CHF study sets both had over 700 patients, and all studies had a different validation sample than the initial exploratory population.  The MayNet incorporates prior clustering, and sample extraction features in its application.   We now report on a new classification method and its application to diagnosis of acute myocardial infarction (AMI).  This method is based on the combination of clustering by Euclidean distances in multi-dimensional space and non-linear discrimination fulfilled by the Artificial Neural Network (ANN) trained on clusters’ averages.   These studies indicate that at an optimum clustering distance the number of classes is minimized with efficient training on the ANN. This novel approach to ANN reduces the number of patterns used for ANN learning and works also as an effective tool for smoothing data, removing singularities,  and increasing the accuracy of classification by the ANN. The studies  conducted involve training and testing on separate clinical data sets, which subsequently achieves a high accuracy of diagnosis (97%).

Unlike classification, which assumes the prior definition of borders between classes5,6, clustering procedure includes establishing these borders as a result of processing statistical information and using a given criteria for difference (distance) between classes.  We perform clustering using the geometrical (Euclidean) distance between two points in n-dimensional space, formed by n variables, including both input and output variables. Since this distance assumes compatibility of different variables, the values of all input variables are linearly transformed (scaled) to the range from 0 to 1.

The ANN technique for readers accustomed to classical statistics can be viewed as an extension of multivariate regression analyses with such new features as non-linearity and ability to process categorical data. Categorical (not continuous) variables represent two or more levels, groups, or classes of correspondent feature, and in our case this concept is used to signify patient condition, for example existence or not of AMI.

The ANN is an acyclic directed graph with input and output nodes corresponding respectively to input and output variables. There are also “intermediate” nodes, comprising so called “hidden” layers.  Each node nj is assigned the value xj that has been evaluated by the node’s “processing” element, as a non-linear function of the weighted sum of values xi of nodes ni, connected with nj by directed edges (ni, nj).

xj = f(wi(1),jxi(1) + wi(2),jxi(2) + … + wi(l),jxi(l)),

where xk is the value in node nk and wk,j is the “weight” of the edge (nk, nj).  In our research we used the standard function f(x), “sigmoid”, defined as f(x)=1/(1+exp(-x)).  This function is suitable for categorical output and allows for using an efficient back-propagation algorithm7 for calculating the optimal values of weights, providing the best fit for learning set of data, and eventually the most accurate classification.

Process description:  We implemented the proposed algorithm for diagnosis of AMI. All the calculations were performed on PC with Pentium 3 Processor applying the authors’ unique Software Agent Maynet. First, using the automatic random extraction procedure, the initial data set (139 patients) was partitioned into two sets — training and testing.  This randomization also determined the size of these sets (96 and 43, respectively) since the program was instructed to assign approximately 70 % of data to the training set.

The main process consists of three successive steps: (1) clustering performed on training data set, (2) neural network’s training on clusters from previous step, and (3) classifier’s accuracy evaluation on testing data.

The classifier in this research will be the ANN, created on step 2, with output in the range [0,1], that provides binary result (1 – AMI, 0 – not AMI), using decision point 0.5.

In this demonstartion we used the data of two previous studies1,2 with three patients, potential outliers, removed (n = 139). The data contains three input variables, CK-MB, LD-1, LD-1/total LD, and one output variable, diagnoses, coded as 1 (for AMI) or 0 (non-AMI).

Results: The application of this software intelligent agent is first demonstrated here using the initial model. Figures 1-2 illustrate the history of training process. One function is the maximum (among training patterns) and lower function shows the average error. The latter defines duration of training process. Training terminates when the average error achieves 5%.

There was slow convergence of back-propagation algorithm applied to the training set of 96 patients. We needed 6800 iterations to achieve the sufficiently small (5%) average error.

Figure 1 shows the process of training on stage 2. It illustrates rapid convergence because we deal only with 9 patterns representing the 9 classes, formed on step 1.

Table 1 illustrates the effect of selection of maximum distance on the number of classes formed and on the production of errors. The number of classes increased with decreasing distance, but accuracy of classification does not decreased.

The rate of learning is inversely related to the number of classes. The use of the back-propagation to train on the entire data set without prior processing is slower than for the training on patterns.

     Figures 2 is a two-dimensional projection of three-dimensional space of input variables CKMB and LD1 with small dots corresponding to the patterns and rectangular as cluster centroids (black – AMI, white – not AMI).

     We carried out a larger study using troponin I (instead of LD1) and CKMB for the diagnosis of myocardial infarction (MI).  The probabilities and odds-ratios for the TnI scaled into intervals near the entropy decision point are shown in Table 2 (N = 782).  The cross-table shows the frequencies for scaled TnI results versus the observed MI, the percent of values within MI, and the predicted probabilities and odds-ratios for MI within TnI intervals.  The optimum decision point is at or near 0.61 mg/L (the probability of MI at 0.46-0.6 mg/L is 3% and the odds ratio is at 13, while the probability of MI at 0.61-0.75 mg/L is 26% at an odds ratio of 174) by regressing the scaled values.

     The RBC, MCV criteria used were applied to a series of 40 patients different than that used in deriving the cutoffs.  A latent class cluster analysis is shown in Table 3.  MayNet is carried out on all 3 data sets for MI, CHF, and for beta thalassemia for comparison and will be shown.

Discussion:  CKMB has been heavily used for a long time to determine heart attacks. It is used in conjunction with a troponin test and the EKG to identify MI but, it isn’t as sensitive as is needed. A joint committee of the AmericanCollege of Cardiology and European Society of Cardiology (ACC/ESC) has established the criteria for acute, recent or evolving AMI predicated on a typical increase in troponin in the clinical setting of myocardial ischemia (1), which includes the 99th percentile of a healthy normal population. The improper selection of a troponin decision value is, however, likely to increase over use of hospital resources.  A study by Zarich8 showed that using an MI cutoff concentration for TnT from a non-acute coronary syndrome (ACS) reference improves risk stratification, but fails to detect a positive TnT in 11.7% of subjects with an ACS syndrome8. The specificity of the test increased from 88.4% to 96.7% with corresponding negative predictive values of 99.7% and 96.2%. Lin et al.9 recently reported that the use of low reference cutoffs suggested by the new guidelines results in markedly increased TnI-positive cases overall. Associated with a positive TnI and a negative CKMB, these cases are most likely false positive for MI. Maynet relieves this and the following problem effectively.

Monitoring BNP levels is a new and highly efficient way of diagnosing CHF as well as excluding non-cardiac causes of shortness of breath. Listening to breath sounds is only accurate when the disease is advanced to the stage in which the pumping function of the heart is impaired. The pumping of the heart is impaired when the circulation pressure increases above the osmotic pressure of the blood proteins that keep fluid in the circulation, causing fluid to pass into the lung’s airspaces.  Our studies combine the BNP with the EKG measurement of QRS duration to predict whether a patient has a high or low ejection fraction, a measure to stage the severity of CHF.

We also had to integrate the information from the hemogram (RBC, MCV) with the hemoglobin A2 quantitation (BioRad Variant II) for the diagnosis of beta thalassemia.  We chose an approach to the data that requires no assumption about the distribution of test values or the variances.   Our detailed analyses validates an approach to thalassemia screening that has been widely used, the Mentzer index10, and in addition uses critical decision values for the tests that are used in the Mentzer index. We also showed that Hgb S has an effect on both Hgb A2 and Hgb F.  This study is adequately powered to assess the usefulness of the Hgb A2 criteria but not adequately powered to assess thalassemias with elevated Hgb F.

References:

1.  Adan J, Bernstein LH, Babb J. Lactate dehydrogenase isoenzyme-1/total ratio: accurate for determining the existence of myocardial infarction. Clin Chem 1986;32:624-8.

2. Rudolph RA, Bernstein LH, Babb J. Information induction for predicting acute myocardial infarction.  Clin Chem 1988;34:2031- 2038.

3. Magidson J. “Maximum Likelihood Assessment of Clinical Trials Based on an Ordered Categorical Response.” Drug Information Journal, Maple Glen, PA: Drug Information Association 1996;309[1]: 143-170.

4. Magidson J and Vermoent J.  Latent Class Cluster Analysis. in J. A. Hagenaars and A. L. McCutcheon (eds.), Applied Latent Class Analysis. Cambridge: CambridgeUniversity Press, 2002, pp. 89-106.

5. Mkhitarian VS, Mayzlin IE, Troshin LI, Borisenko LV. Classification of the base objects upon integral parameters of the attached network. Applied Mathematics and Computers.  Moscow, USSR: Statistika, 1976: 118-24.

6.Mayzlin IE, Mkhitarian VS. Determining the optimal bounds for objects of different classes. In: Dubrow AM, ed. Computational Mathematics and Applications. MoscowUSSR: Economics and Statistics Institute. 1976: 102-105.

7. RumelhartDE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In:

RumelhartDE, Mc Clelland JL, eds. Parallel distributed processing.   Cambridge, Mass: MIT Press, 1986; 1: 318-62.

8. Zarich SW, Bradley K, Mayall ID, Bernstein, LH. Minor Elevations in Troponin T Values Enhance Risk Assessment in Emergency Department Patients with Suspected Myocardial Ischemia: Analysis of Novel Troponin T Cut-off Values.  Clin Chim Acta 2004 (in press).

9. Lin JC, Apple FS, Murakami MM, Luepker RV.  Rates of positive cardiac troponin I and creatine kinase MB mass among patients hospitalized for suspected acute coronary syndromes.  Clin Chem 2004;50:333-338.

10.Makris PE. Utilization of a new index to distinguish heterozygous thalassemic syndromes: comparison of its specificity to five other discriminants.Blood Cells. 1989;15(3):497-506.

Acknowledgements:   Jerard Kneifati-Hayek and Madeleine Schlefer, Midwood High School, Brooklyn, and Salman Haq, Cardiology Fellow, Methodist Hospital.

Table 1. Effect of selection of maximum distance on the number of classes formed and on the accuracy of recognition by ANN

ClusteringDistanceFactor F(D = F * R)  Number ofClasses  Number of Nodes inThe HiddenLayers  Number ofMisrecognizedPatterns inThe TestingSet of 43 Percent ofMisrecognized   
  10.90.80.7  2414135  1, 02, 03, 01, 02, 03, 0

3, 2

3, 2

121121

1

1

2.34.62.32.34.62.3

2.3

2.3

Figure 1.

Figure 2.

Table 2.  Frequency cross-table, probabilities and odds-ratios for scaled TnI versus expected diagnosis

Range Not MI MI N Pct in MI Prob by TnI Odds Ratio
< 0.45 655 2 657 2 0 1
0.46-0.6 7 0 7 0 0.03 13
0.61-0.75 4 0 4 0. 0.26 175
0.76-0.9 13 59 72 57.3 0.82 2307
> 0.9 0 42 42 40.8 0.98 30482
679 103 782 100

 

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