Background: Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10-45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. Results: We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed. Conclusions: This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1010-4) contains supplementary material, which is available to authorized users.

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.

21 22 Background 23 Recent innovations in single-cell Assay for Transposase Accessible Chromatin using 24 sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of 25 individual cells. scATAC-seq data analysis presents unique methodological challenges. 26 scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in 27 humans) lead to inherent data sparsity (1-10% of peaks detected per cell) compared to 28 transcriptomic (scRNA-seq) data (20-50% of expressed genes detected per cell). Such 29 challenges in data generation emphasize the need for informative features to assess cell 30 heterogeneity at the chromatin level. 31 2 32Results 33We present a benchmarking framework that was applied to 10 computational methods 34for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell 35 types from diverse tissues and organisms. Methods for processing and featurizing 36 scATAC-seq data were evaluated by their ability to discriminate cell types when 37 combined with common unsupervised clustering approaches. We rank evaluated 38 methods and discuss computational challenges associated with scATAC-seq analysis 39including inherently sparse data, determination of features, peak calling, the effects of 40 sequencing coverage and noise, and clustering performance. Running times and 41 memory requirements are also discussed. 42 43Conclusions 44This reference summary of scATAC-seq methods offers recommendations for best 45 practices with consideration for both the non-expert user and the methods developer. 46Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and 47 cisTopic outperform other methods in separating cell populations of different coverages 48 and noise levels in both synthetic and real datasets. Notably, SnapATAC was the only 49 method able to analyze a large dataset (> 80,000 cells). 50 51

The work by Tang et al. provides a comprehensive, single-cell, transcriptomic analysis of kidney and blood cells from the adult zebrafish, identifying novel cell types, including two classes of NK immune cells, classically defined and erythroid-primed hematopoietic stem and progenitor cells, mucin-secreting kidney cells, and kidney stem/progenitor cells.

Highlights d A living biobank of CAFs from NSCLC patients recapitulates clinical CAF heterogeneity d Therapeutic profiling of the NSCLC CAFs reveals three distinctive functional subtypes d Subtype I and II CAFs have high HGF and FGF7 expression and protect cancer cells d Subtype III CAFs associate with better clinical response and immune cell migration

BackgroundHuman pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved.ResultsWe use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells.ConclusionsOur single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1426-0) contains supplementary material, which is available to authorized users.

SUMMARY Aging is closely associated with increased susceptibility to breast cancer, yet there have been limited systematic studies of aging-induced alterations in the mammary gland. Here, we leverage high-throughput single-cell RNA sequencing to generate a detailed transcriptomic atlas of young and aged murine mammary tissues. By analyzing epithelial, stromal, and immune cells, we identify age-dependent alterations in cell proportions and gene expression, providing evidence that suggests alveolar maturation and physiological decline. The analysis also uncovers potential pro-tumorigenic mechanisms coupled to the age-associated loss of tumor suppressor function and change in microenvironment. In addition, we identify a rare, age-dependent luminal population co-expressing hormone-sensing and secretory-alveolar lineage markers, as well as two macrophage populations expressing distinct gene signatures, underscoring the complex heterogeneity of the mammary epithelia and stroma. Collectively, this rich single-cell atlas reveals the effects of aging on mammary physiology and can serve as a useful resource for understanding aging-associated cancer risk.