The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Comprehensive Integration of Single-Cell Data.

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

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Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Bone marrow houses a multifunctional stromal cell population expressing C-X-C motif chemokine ligand 12 (CXCL12), termed CXCL12-abundant reticular (CAR) cells, that regulates osteogenesis and adipogenesis. The quiescent pre-adipocyte-like subset of CXCL12+ stromal cells ("Adipo-CAR" cells) is localized to sinusoidal surfaces and particularly enriched for hematopoiesis-supporting cytokines. However, detailed characteristics of these CXCL12+ pre-adipocyte-like stromal cells and how they contribute to marrow adipogenesis remain largely unknown. Here we highlight CXCL12-dependent physical coupling with hematopoietic cells as a potential mechanism regulating the adipogenic potential of CXCL12+ stromal cells. Single-cell computational analyses of RNA velocity and cell signaling reveal that Adipo-CAR cells exuberantly communicate with hematopoietic cells through CXCL12-CXCR4 ligand-receptor interactions but do not interconvert with Osteo-CAR cells. Consistent with this computational prediction, a substantial fraction of Cxcl12-creER+ pre-adipocyte-like cells intertwines with hematopoietic cells in vivo and in single-cell preparation in a protease-sensitive manner. Deletion of CXCL12 in these cells using Col2a1-cre leads to a reduction of stromal-hematopoietic coupling and extensive marrow adipogenesis in adult bone marrow, which appears to involve direct conversion of CXCL12+ cells to lipid-laden marrow adipocytes without altering mesenchymal progenitor cell fates. Therefore, these findings suggest that CXCL12+ pre-adipocyte-like marrow stromal cells prevent their premature differentiation by maintaining physical coupling with hematopoietic cells in a CXCL12-dependent manner, highlighting a possible cell-non-autonomous mechanism that regulates marrow adipogenesis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Intercellular Interactions of an Adipogenic CXCL12 ‐Expressing Stromal Cell Subset in Murine Bone Marrow

Single cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.

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Manifold Alignment Reveals Correspondence Between Single Cell Transcriptome and Epigenome Dynamics

Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously employed integrative non-negative matrix factorization (iNMF) to jointly align multiple single-cell datasets (\(X_i\)) and learn interpretable low-dimensional representations using dataset-specific (\(V_i)\) and shared metagene factors (W) and cell factor loadings (\(H_i\)). We developed an alternating nonnegative least squares (ANLS) algorithm to solve the iNMF optimization problem [2]:

Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning

A key problem in computational biology is dis-covering the gene expression changes that regu-late cell fate transitions, in which one cell type turns into another. However, each individual cell cannot be tracked longitudinally, and cells at the same point in real time may be at different stages of the transition process. This can be viewed as a problem of learning the behavior of a dynamical system from observations whose times are unknown. Additionally, a single progenitor cell type often bifurcates into multiple child cell types, further complicating the problem of modeling the dynamics. To address this problem, we developed an approach called variational mixtures of ordinary differential equations. By using a simple family of ODEs informed by the biochemistry of gene expression to constrain the likelihood of a deep generative model, we can simultaneously infer the latent time and latent state of each cell and predict its future gene expression state. The model can be interpreted as a mixture of ODEs whose parameters vary continuously across a latent space of cell states. Our approach dramatically improves data fit, latent time inference, and future cell state estimation of single-cell gene expression data compared to previous approaches.

Joshua D. Welch

Papers

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Intercellular Interactions of an Adipogenic CXCL12 ‐Expressing Stromal Cell Subset in Murine Bone Marrow

Manifold Alignment Reveals Correspondence Between Single Cell Transcriptome and Epigenome Dynamics

Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning

Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics