Archive for the ‘Single Cell Genomics’ Category

Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD


scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060


Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples



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Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date.  The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing.  In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants.  The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

  • Copy number variations
  • Chromosomal alterations
  • Gene fusions
  • Deletions
  • Gene duplications
  • Insertions
  • Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods:  The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line.  A split read and within-read mapping approach was used to detect variants of different types and sizes.  In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

  1. Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
  2. The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
  3. Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
  4. Found 1,493 long read variants, 603 of which were between different chromosomes
  5. Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is  GeneCard for each gene.

SplitThreader path


# Genes Distance
of variants
in path
Previously observed in references
1 KLHDC2 SNTB1 9837 3 14|17|8 Asmann et al. (2011) as only a 2-hop fusion
2 CYTH1 EIF3H 8654 2 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); RNA only, not observed as 2-hop
3 CPNE1 PREX1 1777 2 20 Found and validated as 2-hop by Chen et al. 2013
4 GSDMB TATDN1 0 1 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
5 LINC00536 PVT1 0 1 8 No
6 MTBP SAMD12 0 1 8 Validated by Edgren et al. (2011)
7 LRRFIP2 SUMF1 0 1 3 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
8 FBXL7 TRIO 0 1 5 No
9 ATAD5 TLK2 0 1 17 No
10 DHX35 ITCH 0 1 20 Validated by Edgren et al. (2011)
11 LMCD1-AS1 MECOM 0 1 3 No
12 PHF20 RP4-723E3.1 0 1 20 No
13 RAD51B SEMA6D 0 1 14|15 No
14 STAU1 TOX2 0 1 20 No
15 TBC1D31 ZNF704 0 1 8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011); Chen et al. (2013)


SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.




  1. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
  2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
  3. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
  4. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.


Other articles on Cancer Genome Sequencing in this Open Access Journal Include:


International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to


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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.


One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.


Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.


The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.


This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.


Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.















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Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab


Reporter: Aviva Lev-Ari, PhD, RN

A lecture by Dana Pe’er is included, below in the eProceedings which I generated in Real Time on 6/14/2019 @MIT

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA




Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI



Research Programs

Cancer Biology & Genetics Program

Our scientists study the molecular and genetic determinants of cancer predisposition, tumor development, and metastasis.

Cell Biology Program

Our researchers explore the molecular mechanisms that control normal cell behavior and how these mechanisms are disrupted in cancer.

Chemical Biology Program

Our scientists use chemical principles to investigate cutting-edge topics in biology and medicine.

Computational & Systems Biology Program

The goal of our research is to build computer models that simulate biological processes, from the molecular level up to the organism as a whole.

Developmental Biology Program

Our investigators study the mechanisms that control cell proliferation, cell differentiation, tissue patterning, and tissue morphogenesis.

Immunology Program

Our research is geared toward understanding how the immune system functions in all its complexity and how it can be harnessed to fight disease.

Molecular Biology Program

Our research is directed at understanding how cell growth is regulated and how the integrity of the genome is maintained.

Molecular Pharmacology Program

Our research program serves as a conduit for bringing basic science discoveries to preclinical and clinical evaluation.

Structural Biology Program

Our researchers are dedicated to understanding biology at the structural and mechanistic levels, and aiding the development of new cancer therapies.

Book traversal links for Research


The Dana Pe’er Lab


The Dana Pe'er Lab

The Pe’er lab combines single cell technologies, genomic datasets and machine learning algorithms to address fundamental questions in biomedical science. Empowered by recent breakthrough technologies like massive parallel single cell RNA-sequencing, we ask questions such as: How do multi-cellular organisms develop from a single cell, resulting in the vast diversity of progenitor and terminal cell types? How does a cell’s regulatory circuit control the dynamics of signal processing and how do these circuits rewire over the course of development? How does an ensemble of cells function together to execute a multi-cellular response, such as an immune response to pathogen or cancer? We will also address more medically oriented questions such as: How do regulatory circuits go awry in disease? What is the consequence of intra-tumor heterogeneity? Can we characterize the tumor immune eco-system to gain a better understanding of when or why immunotherapy works or does not work? A key goal is to use this characterization of the tumor immune eco-system to personalize immunotherapy.

Dana Pe'er, PhD

Dana Pe’er, PhD

Chair, Computational and Systems Biology Program, SKI; Scientific Director, Metastasis & Tumor Ecosystems Center

Research Focus

Computational Biologist Dana Pe’er combines single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine.


PhD, Hebrew University, Jerusalem Israel


The Dana Pe’er Lab: Publications

View a full listing of Dana Pe’er’s journal articles.

Palantir characterizes cell fate continuities in human hematopoiesis. Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe’er D. 2019, in press. Nature Biotechnology.

Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme RM, Dao P, McKenney PT, Wasti RC, Kadaveru K, Mazutis L, Rudensky AY, Pe’er D. Cell. 2018 Aug 23;174(5):1293-1308.e36. doi: 10.1016/j.cell.2018.05.060. PMID: 29961579

Recovering gene interactions from single-cell data using data diffusion. van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. PubMed PMID: 29961576

The Human Cell Atlas. Regev A et al. Elife. 2017 Dec 5;6. pii: e27041. doi: 10.7554/eLife.27041. PubMed PMID: 29206104

Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, Sharma P, Wang J, Wargo JA, Pe’er D, Allison JP. Cell. 2017 Sep 7;170(6):1120-1133.e17. doi: 10.1016/j.cell.2017.07.024. PMID: 28803728

Wishbone identifies bifurcating developmental trajectories from single-cell data. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe’er D. Nat Biotechnol. 2016 Jun;34(6):637-45. doi: 10.1038/nbt.3569. PMID: 27136076

Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe’er D, Nolan GP. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. PMID: 26095251

Interferon α/β enhances the cytotoxic response of MEK inhibition in melanoma. Litvin O, Schwartz S, Wan Z, Schild T, Rocco M, Oh NL, Chen BJ, Goddard N, Pratilas C, Pe’er D. Mol Cell. 2015 Mar 5;57(5):784-796. doi: 10.1016/j.molcel.2014.12.030. PMID: 25684207

Integration of genomic data enables selective discovery of breast cancer drivers. Sanchez-Garcia F, Villagrasa P, Matsui J, Kotliar D, Castro V, Akavia UD, Chen BJ, Saucedo-Cuevas L, Rodriguez Barrueco R, Llobet-Navas D, Silva JM, Pe’er D. Cell. 2014 Dec 4;159(6):1461-75. doi: 10.1016/j.cell.2014.10.048. PMID: 25433701

Conditional density-based analysis of T cell signaling in single-cell data. Krishnaswamy S, Spitzer MH, Mingueneau M, Bendall SC, Litvin O, Stone E, Pe’er D, Nolan GP. Systems biology. Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. PMID: 25342659

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe’er D. Cell. 2014 Apr 24;157(3):714-25. doi: 10.1016/j.cell.2014.04.005. PMID: 24766814

Book traversal links for The Dana Pe’er Lab



The Dana Pe’er Lab is one of four Labs of the Computational & Systems Biology Program

Computational biologists combine findings in biology with computer algorithms and databases to conduct biological research on powerful computers, using sophisticated software — so-called “dry” laboratories — in ways that complement and strengthen traditional laboratory and clinical research. The aim is to build computer models that simulate biological processes from the molecular level up to the organism as a whole and to use these models to make useful predictions.


Computational biology can help interpret detailed molecular profiles of cancerous and noncancerous cells, molecular response profiles of therapeutic agents, and a person’s genetic profile to assist in the development of better diagnostics and prognostics, as well as improved therapies. Intelligent use of computational methods using detailed molecular and genomic data is expected to reduce the trial and error of drug development and possibly lead to shorter, more accurate clinical trials.


The Christina Leslie Lab

The John Chodera Lab

The Dana Pe'er Lab

The Joao Xavier Lab


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Norwich Single-Cell Symposium 2019, Earlham Institute, single-cell genomics technologies and their application in microbial, plant, animal and human health and disease, October 16-17, 2019, 10AM-5PM

Reporter: Aviva Lev-Ari, PhD, RN




Earlham Institute
Norwich Research Park
Norwich, Norfolk

About the event.

Single cell genomic technologies continue to develop at pace, generating new insights into cellular diversity within living systems. The Norwich Single Cell Symposium, hosted annually at EI, aims to bring together researchers applying single cell technologies across a wide range of species.

Now in its third year, the Symposium covers single-cell genomics technologies and their application in microbial, plant, animal and human health and disease. The symposium offers a forum for researchers to discuss the latest developments in single-cell genomics, and network with other researchers with the intention of catalysing future development and application of single-cell genomics across the UK.

This year, the event will take place over two days and feature talks from invited speakers and selected abstracts, and we are keen to capture as broad a range of single-cell applications as possible.

Topics to be covered include:

  • Single cell genomics in plant and microbial research
  • Single cell genomics in health, disease and development
  • Single cell informatics
  • Single cell technology development


This year we are offering the opportunity to present posters on your research during the canapes and networking session. Submission deadline for abstracts is 1 September 2019 (23.59 GMT).

All abstracts must be submitted electronically as part of your registration. Submissions via email will not be accepted

Abstract limits are 250 words (excluding title, authors and affiliations)

Presenting author should be highlighted in bold

Abstracts will be reviewed within 14 days of the submission deadline and you will be informed of the outcome shortly thereafter

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Three Technology Leaders in Single Cell Sequencing: 10X Genomics, Illumina and MissionBio


Reporter: Aviva Lev-Ari, PhD, RN

We review below only Three Technology Leaders in Single Cell Sequencing. There are other players in this research technology space.



By Nicole Davis, Ph.D.




Platform Solution

Tapestri: The Precision Genomics Platform.


“Single-cell DNA analysis

allows us to actually observe

the clonal composition of

cancer, instead of guessing at

it. This opens up the possibility

of being able to make dynamic

changes in treatment.”

– Catherine Smith, MD, UCSF


Move Beyond Bulk NGS to Precision Genomics

The average read-out from conventional bulk sequencing misses the rare events and underlying genetic

diversity within and across cell populations. To resolve heterogeneity and improve patient stratification,

therapy selection, and disease monitoring we need insights into mutation co-occurrence cell population,

and zygosity within every single cell.

Clonal Resolution with Single-Cell Precision

Complex disease evolves, so understanding genetic variability — including mutation co-occurrence at the single-cell level — is vitally important for clinical researchers to break the cycle of treatment response, resistance and relapse.


High Sensitivity to Reveal True Heterogeneity

The Tapestri Platform revolutionizes the capability to directly assess the clonal architecture of a sample with detection of mutation co-occurrence patterns. Rather than inferring variants that co-occur within a subclone from comparable bulk variant allele frequencies, single-cell resolution uncovers the true distribution of genotypes and their segregation patterns across subclones.

Platform Features

  • Targeted and accurate SNVs and indel variant calling
  • Single-cell DNA throughput up to 10,000 cells
  • Simple workflow
  • User-friendly bioinformatics software
  • Customizable content
  • Detect rare subclones down to 0.1%
  • Resolve clonal architecture
  • Identify mutation co-occurrence
  • How the Tapestri Platform Works

    Step 3: After running your sequencer, proceed with our downstream analysis and visualization software. Mission Bio provides dedicated bioinformatics support to help you discover biological and clinical insights


A highly sensitive method for measuring gene expression from single cells

Generating RNA-Seq libraries from single cell and ultra-low-input samples

Illumina’s Single cell sequencing technologies

Single-Cell Research An Overview of Recent Single-Cell Research Publications Featuring Illumina® Technology

  • Quartz-Seq
  • Smart-Seq
  • Smart-Seq2
  • Single-Cell Methylome & Transcriptome Sequencing
  • Genome & Transcriptome Sequencing
  • Genomic DNA and mRNA Sequencing
  • T Cell–Receptor Chain Pairing
  • Unique Molecular Identifiers
  • Cell Expression by Linear Amplification Sequencing
  • Flow Cell–Surface Reverse-Transcription Sequencing
  • Single-Cell Tagged ReverseTranscription Sequencing
  • Fixed and Recovered Intact Single-Cell RNA Sequencing
  • Cell Labeling via Photobleaching Indexing Droplets
  • Drop-Seq
  • CytoSeq
  • Single-Cell RNA Barcoding and Sequencing
  • High-Throughput Single-Cell Labeling



5 Introduction

7 Applications Cancer Metagenomics Stem Cells Developmental Biology Immunology Neurobiology Drug Discovery Reproductive Health Microbial Ecology and Evolution Plant Biology Forensics Allele-Specific Gene Expression 50 Sample Preparation

54 Data Analysis

60 DNA Methods Multiple-Strand Displacement Amplification Genome & Transcriptome Sequencing Multiple Annealing and Looping–Based Amplification Cycles Genomic DNA and mRNA Sequencing

68 Epigenomics Methods Single-Cell Assay for TransposaseAccessible Chromatin Using Sequencing Single-Cell Bisulfite Sequencing/ Single-Cell Whole-Genome Bisulfite Sequencing Single-Cell Methylome & Transcriptome Sequencing Single-Cell Reduced-Representation Bisulfite Sequencing Single-Cell Chromatin Immunoprecipitation Sequencing Chromatin Conformation Capture Sequencing Droplet-Based Chromatin Immunoprecipitation Sequencing

78 RNA Methods Designed Primer–Based RNA Sequencing Single-Cell Universal Poly(A)- Independent RNA Sequencing


PAGES 54 – 56 Data Analysis – most important pages in the Report, $669

Single-cell sequencing poses unique challenges for data analysis. Individual mammalian cells contain 50,000–300,000 transcripts, and gene expression values among individual cells can vary significantly.206 Although several hundred thousand transcripts may be expressed per individual cell, up to 85% of these are present at only 1–100 copies.207 Therefore, it is critically important in scRNA-Seq to capture low-abundance mRNA transcripts and amplify the synthesized cDNA to ensure that all transcripts are ultimately represented uniformly in the library.208,209 Spike-in quantification standards of known abundance can help distinguish technical variability/ noise from biologically meaningful gene expression changes.210 Molecular indexing can also correct for sequencing biases,211.212 and recent improvements in automated sample handling can reduce technical variability even more.213


Genome & Transcriptome Sequencing

Genome & transcriptome sequencing (G&T-Seq) is a protocol that can separate and sequence genomic DNA and full-length mRNA from single cells.276 In this method, single cells are isolated and lysed. RNA is captured using biotinylated oligo(dT) capture primers and separated from DNA using streptavidin-coated magnetic beads. SmartSeq2 is used to amplify captured RNA on the bead, while MDA is used to amplify DNA. After sequencing, integrating DNA and RNA sequences provides insights into the gene-expression profile of single cells (Table 4)


Genomic DNA and mRNA Sequencing

DR-Seq studies the genomic and transcriptomic relationship of single cells via sequencing. Nucleic acid amplification prior to physical separation reduces sample loss and the risk of contamination. DR-Seq involves multiple amplification steps, including the quasilinear amplification technique similar to MALBAC. First, mRNAs are reverse-transcribed from lysed single cells using poly(dT) primers with Ad-1x adapters, producing single-stranded cDNA (sscDNA). The Ad-1x adapter sequence contains cell-identifying barcodes, 5’ Illumina adapters, and a T7 promoter. Next, both gDNA and sscDNA are amplified simultaneously via quasilinear WGA with Ad-2 primers. These primers are similar to MALBAC adapters, containing 8 random nucleotides for random priming followed by a constant 27-nucleotide


Single-Cell Methylome & Transcriptome Sequencing

scM&T-Seq allows parallel analysis of both epigenetic and gene expression patterns from single cells using Smart-Seq2 and scBS-Seq. scM&T-Seq is built upon G&T-Seq, but instead of using MDA for DNA sequencing, it uses scBS-Seq to interrogate DNA methylation patterns. First, single cells are isolated and individually lysed. Then, mRNAs are isolated using streptavidin-coupled mRNA capture primers, physically separating them from DNA strands. Smart-Seq2 is used to generate cDNA libraries from the mRNA, which involves reverse transcription with template switching and tagmentation. DNA libraries are prepared via scBS-Seq, which involves bisulfite conversion of DNA strands to identify methylated cytosines. Both libraries are now ready for sequencing (Table 9).



Low-level RNA detection refers to both detection of rare RNA molecules in a cellfree environment (such as circulating tumor RNA) and the expression patterns of single cells. Tissues consist of a multitude of different cell types, each with a distinctly different set of functions. Even within a single cell type, the transcriptomes are highly dynamic and reflect temporal, spatial, and cell cycle–dependent changes. Cell harvesting, handling, and technical issues with sensitivity and bias during amplification add additional levels of complexity. To resolve this multitiered complexity would require analyzing many thousands of cells. The use of unique barcodes has greatly increased the number of samples that can be multiplexed and pooled at little to no decrease in reads associated with each sample. Recent improvements in cell capture and sample preparation will provide more information, faster, and at lower cost.310,311 This development promises to expand our understanding of cell function fundamentally, with significant implications for research and human health. 312


Genome & Transcriptome Sequencing

G&T-Seq is a protocol that can separate and sequence genomic DNA and full-length mRNA from single cells.327 In this method, single cells are isolated and lysed. RNA is captured using biotinylated oligo(dT) capture primers and separated from DNA using streptavidin-coated magnetic beads. Smart-Seq2 is used to amplify captured RNA on the bead, while MDA is used to amplify DNA. After sequencing, integrating DNA and RNA sequences provides insights into the gene-expression profile of single cells.


Genomic DNA and mRNA Sequencing

DR-Seq studies the genomic and transcriptomic relationship of single cells via sequencing. Nucleic acid amplification prior to physical separation reduces sample loss and the risk of contamination. DR-Seq involves multiple amplification steps, including the quasilinear amplification technique similar to MALBAC.


T Cell–Receptor Chain Pairing

Functional TCRs are heterodimeric proteins composed of unique combinations of α and β chains. For an accurate functional analysis, both subunits must be sequenced together to avoid disrupting the α- and β-chain pairing during the cell lysis step.333


Flow Cell–Surface Reverse-Transcription Sequencing

Flow cell–surface reverse-transcription sequencing (FRT-Seq) is a transcriptomesequencing technique developed in 2010.339 It is strand-specific, free of amplification, and is compatible with paired-end sequencing. To begin with, poly(A)+ RNA samples are fragmented by metal-ion hydrolysis and dephosphorylated.


Single-Cell RNA Barcoding and Sequencing

Single-cell RNA barcoding and sequencing (SCRB-Seq) is a cost-efficient, multiplexed scRNA-Seq technique. SCRB-Seq isolates single cells into wells using FACS. After cell lysis, poly(A)+ mRNAs are annealed to a custom primer containing a poly(T) tract, UMI, well barcode, and biotin. Template-switching reverse transcription and PCR amplification are carried out on the mRNA, generating barcoded full-length cDNA. cDNA strands from all wells are pooled together to be purified. They are amplified by PCR and purified further. cDNA libraries are prepared using the Nextera XT kit with modified i5 primers. The resultant cDNA fragments are size-selected for 300–800 bp and sequenced (Table 31).


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accelerate science

10x Genomics is building tools for scientific discovery that reveal and address the true complexities of biology and disease. Through a combination of novel microfluidics, chemistry and bioinformatics, our award-winning Chromium System is enabling researchers around the world to more fully understand the fundamentals of biology at unprecedented resolution and scale.

Research Area



Beyond Cell Type

For the first time, scRNA-Seq is enabling a cell-by-cell molecular and cellular characterization of hundreds of thousands of cells within the same sample. Complex systems, like those found in the immune system, can be explored without limits.

scRNA-Seq can now be applied to:
  • Immunology
  • Neurology
  • Stem Cell Biology
  • Oncology
  • Immuno-oncology
  • Functional Genomics
V(D)J Recombination

Clarify complex Systems

Single Cell Technology

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SINGLE CELL GENOMICS 2019, September 24-26, 2019, Djurönäset, Stockholm, Sweden

Reporter: Aviva Lev-Ari, PhD, RN




Organizing committee

  • Ido Amit
  • Amos Tanay
  • Sten Linnarsson
  • Rickard Sandberg
  • Aviv Regev
  • John Marioni
  • Alexander van Oudenaarden

Sponsored by:

Single cell genomics has emerged as a revolutionary technology transforming nearly every field of biomedical research. Through its many applications (single cell genome sequencing, single cell transcriptomics, various single cell epigenetic profiling approaches, and spatially resolved methods), researchers can characterize the genetic and functional properties of individual cells in their native conditions, leading to numerous experimental and clinical opportunities. As technology is leaping forward, many critical questions are arising:

• How can the behavior of groups of thousands or tens of thousands of single cells be analyzed and modeled?

• How can samples of precise single-cell-states be converted to inferred cellular behaviour, in space and time?

• How can multimodal single-cell datasets be integrated?

• What can we learn about cell-cell interactions?

• What are the immediate implications to fields like neuroscience, immunology, cancer research and stem cells?

• What will the longer-term impacts be for clinical research and practice?

The conference will bring together many of the pioneers and leading experts in the field to three days of extensive, interdisciplinary and informal discussion. Our goal is to create a forum where knowledge is shared, hoping to define together the agenda of this new community. The meeting will include presentations from invited leaders and several selected abstracts, a poster session and many opportunities for interaction. We encourage students and postdocs to participate by presenting abstracts.


  • Ed Boyden, MIT
  • Long Cai, CalTech
  • Joe Ecker, Salk Institute
  • Guoji Guo, Zhejiang University
  • Shalev Itzkovitz, Weizmann Institute of Science
  • Maria Kasper, Karolinska Institutet
  • Job Kind, Hubrecht institute
  • Allon Klein, Harvard
  • Keren Leeat, Standford
  • Ed Lein, Allen Institute
  • Evan Macosko, Broad Institute
  • Dana Pe’er, MSKCC
  • Nikolaus Rajewsky, Max Delbrück
  • Alex Shalek, MIT
  • Fabian Theis, Helmholtz Munich
  • Barbara Treutlein, Max Planck Institute
  • Hongkui Zeng, Allen Institute
  • Xiaowei Zhuang, Harvard

Program – pending

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