Recent Advances In Single-cell Technologies Research Articles

Single-cell analysis of chromatin accessibility in the adult mouse brain

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1–4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs—specifically, those identified from a subset of cortical excitatory neurons—are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Single-cell resolution analysis reveals the preparation for reprogramming the fate of stem cell niche in cotton lateral meristem

BackgroundSomatic embryogenesis is a major process for plant regeneration. However, cell communication and the gene regulatory network responsible for cell reprogramming during somatic embryogenesis are still largely unclear. Recent advances in single-cell technologies enable us to explore the mechanism of plant regeneration at single-cell resolution.ResultsWe generate a high-resolution single-cell transcriptomic landscape of hypocotyl tissue from the highly regenerable cotton genotype Jin668 and the recalcitrant TM-1. We identify nine putative cell clusters and 23 cluster-specific marker genes for both cultivars. We find that the primary vascular cell is the major cell type that undergoes cell fate transition in response to external stimulation. Further developmental trajectory and gene regulatory network analysis of these cell clusters reveals that a total of 41 hormone response-related genes, including LAX2, LAX1, and LOX3, exhibit different expression patterns in the primary xylem and cambium region of Jin668 and TM-1. We also identify novel genes, including CSEF, PIS1, AFB2, ATHB2, PLC2, and PLT3, that are involved in regeneration. We demonstrate that LAX2, LAX1 and LOX3 play important roles in callus proliferation and plant regeneration by CRISPR/Cas9 editing and overexpression assay.ConclusionsThis study provides novel insights on the role of the regulatory network in cell fate transition and reprogramming during plant regeneration driven by somatic embryogenesis.

Monocytes in Tumorigenesis and Tumor Immunotherapy.

Monocytes are highly plastic innate immune cells that display significant heterogeneity during homeostasis, inflammation, and tumorigenesis. Tumor-induced systemic and local microenvironmental changes influence the phenotype, differentiation, and distribution of monocytes. Meanwhile, monocytes and their related cell subsets perform an important regulatory role in the development of many cancers by affecting tumor growth or metastasis. Thanks to recent advances in single-cell technologies, the nature of monocyte heterogeneity and subset-specific functions have become increasingly clear, making it possible to systematically analyze subset-specific roles of monocytes in tumorigenesis. In this review, we discuss recent discoveries related to monocytes and tumorigenesis, and new strategies for tumor biomarker identification and anti-tumor immunotherapy.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

Best practices for single-cell analysis across modalities.

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT

Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and impute multimodal datasets with mosaic measurements. A key advance is the use of a missing mask for learning the conditional distribution of unobserved modalities and features, which makes scVAEIT flexible to combine different panels of measurements from multimodal datasets accurately and in an end-to-end manner. Imputing the masked features serves as a supervised learning procedure while preventing overfitting by regularization. Focusing on gene expression, protein abundance, and chromatin accessibility, we validate that scVAEIT robustly imputes the missing modalities and features of cells biologically different from the training data. scVAEIT also adjusts for batch effects while maintaining the biological variation, which provides better latent representations for the integrated datasets. We demonstrate that scVAEIT significantly improves integration and imputation across unseen cell types, different technologies, and different tissues.

EPCO-16. ESTIMATING THE DIFFERENTIATION POTENTIAL AND PLASTICITY OF GLIOBLASTOMA CELLS USING STATISTICAL MECHANICS

Abstract Glioblastoma (GBM) is the most common form of adult brain tumors and patients diagnosed with GBM face a very dismal prognosis with a median survival rate of 15 months. The challenges in treating GBM are many, there is a complex cellular and molecular heterogeneity, both between patients and within individual tumors. In a single tumor, individual cancer cells adopt a variety of stem-like, progenitor-like, and more differentiated phenotypes (a.k.a. cell states). Recent advances in single-cell technology have made it possible to detect this diversity of cellular states, but yet we lack a good explanation for how the states arise and if transitions between states can be exploited to therapeutic ends. We here propose a new computational strategy to measure the differentiation potential of GBM cells, based on the Ising models from statistical mechanics. Originally used to model coupled magnets on a lattice, the Ising models can be generalized to represent the probability of a particular configuration of any system of n variables. We show that the models can be fitted to high-dimensional single cell data in a matter of seconds by solving a convex optimization problem, and that the models help explain cell differentiation in terms of minimization of an energy function of the system. In our on-going work we have fitted the model to single cell data from our previous work on state transitions in GBM (Larsson, Dalmo et al, Molecular Systems Biology 2021) and shown that the predicted differentiation potential of cell states is consistent with the cell state hierarchies derived from lineage tracing experiments. Moving forward, we are now exploring how the Ising models can be used to model state transitions in GBM and to predict the effect of targeted therapy on the cell’s differentiation potential.

Approaches to benchmark and characterize in vitro human model systems.

In vitro human models, such as gastruloids and organoids, are complex three-dimensional (3D) structures often consist of cells from multiple germ layers that possess some attributes of a developing embryo or organ. To use these models to interrogate human development and organogenesis, these 3D models must accurately recapitulate aspects of their in vivo counterparts. Recent advances in single-cell technologies, including sequencing and spatial approaches, have enabled efforts to better understand and directly compare organoids with native tissues. For example, single-cell genomic efforts have created cell and organ atlases that enable benchmarking of in vitro models and can also be leveraged to gain novel biological insights that can be used to further improve in vitro models. This Spotlight discusses the state of current in vitro model systems, the efforts to create large publicly available atlases of the developing human and how these data are being used to improve organoids. Limitations and perspectives on future efforts are also discussed.

Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors.

Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.

Abstract 2125: Towards a spatial view of immune cell function in cancer

Abstract Immunotherapy can result in lasting tumor regressions, but despite its success, only a subset of patients and cancer types benefit from immunotherapy. Thus, uncovering features of the tumor microenvironment (TME) that contribute to this differential response can inform candidates for novel therapies and biomarkers for patient stratification. Recent advances in single-cell technologies allow for profiling of cell states and their spatial interactions within a tumor. Here, we performed multiplexed immunofluorescence (mpIF), a method for in situ single-cell measurement of 30+ proteins, as well as bulk transcriptomics and genomics on 180 tumor samples across 4 immunotherapy-responsive and -resistant solid and liquid cancers: in-transit melanoma (ITM), non-small cell lung cancer (NSCLC), glioblastoma multiforme (GBM), and classical Hodgkin lymphoma (cHL). In aggregate, we measured over 100 million single cells and identified over 1,000 unique tumor and immune cell states. We uncovered immune-suppressive cell states, such as macrophages negative for PD-L1 and B7-H3 in immunotherapy-resistant ITM tumors and all GBM tumors, which generally fail to respond to immunotherapy. In GBM, macrophages exhibited two distinct spatial topologies, infiltrated or excluded. In ITM, we identified pre-treatment cell states and gene expression signatures that associate with immunotherapy response such as MHC class I expression on the tumor cell surface, B cell aggregates, “exhausted” PD-1/LAG-3/TIM-3 triple-positive CD8+ T cells, and expression of interferon-gamma genes. We observed a similar immune checkpoint-rich (PD-1/LAG-3/TIM-3 triple-positive) TME in all cHL tumors, which have remarkably high immunotherapy response rates. We spatially defined the microniche (30-micron radius neighborhood) of “exhausted” CD8+ T cells, and within the microniches, found antigen presentation competent tumor cells, proliferating T cells, and B7-H3 positive macrophages, which together contribute to an activated TME. Together, we present a statistical workflow for the integrated analysis of spatially resolved multidimensional data for cancer target discovery that is tailored towards application in routinely collected formalin-fixed paraffin-embedded cancer biospecimens. Citation Format: Maryam Pourmaleki, Caitlin J. Jones, Brian D. Greenstein, Sabrina D. Mellinghoff, Daniel A. Navarrete, Smrutiben A. Mehta, Nicholas D. Socci, Ingo K. Mellinghoff, Travis J. Hollmann. Towards a spatial view of immune cell function in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2125.

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment.

Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

Hematopoiesis under telomere attrition at the single-cell resolution

The molecular mechanisms that drive hematopoietic stem cell functional decline under conditions of telomere shortening are not completely understood. In light of recent advances in single-cell technologies, we sought to redefine the transcriptional and epigenetic landscape of mouse and human hematopoietic stem cells under telomere attrition, as induced by pathogenic germline variants in telomerase complex genes. Here, we show that telomere attrition maintains hematopoietic stem cells under persistent metabolic activation and differentiation towards the megakaryocytic lineage through the cell-intrinsic upregulation of the innate immune signaling response, which directly compromises hematopoietic stem cells’ self-renewal capabilities and eventually leads to their exhaustion. Mechanistically, we demonstrate that targeting members of the Ifi20x/IFI16 family of cytosolic DNA sensors using the oligodeoxynucleotide A151, which comprises four repeats of the TTAGGG motif of the telomeric DNA, overcomes interferon signaling activation in telomere-dysfunctional hematopoietic stem cells and these cells’ skewed differentiation towards the megakaryocytic lineage. This study challenges the historical hypothesis that telomere attrition limits the proliferative potential of hematopoietic stem cells by inducing apoptosis, autophagy, or senescence, and suggests that targeting IFI16 signaling axis might prevent hematopoietic stem cell functional decline in conditions affecting telomere maintenance.

Using Mass Cytometry to Address Tfh and Tfr Heterogeneity.

Recent advances in single-cell technologies have made it possible to gather increased amounts of information about even rare cell subtypes. In particular, mass cytometry is able to assess the expression of 30-50 proteins on millions of cells. Within CD4 T-cells, T-follicular helper cells (Tfh) and their regulatory counterpart, T-follicular regulatory cells (Tfr), localize to the B-cell follicle and have specialized roles in the maintenance and regulation of B-cell antibody production. The frequency of Tfh and Tfr in circulation has also been associated with ongoing antibody responses.In this chapter, we detail methods to analyze the frequency and phenotype of the populations of Tfh and Tfr found in humans by mass cytometry.

Glomerular Kidney Diseases in the Single-Cell Era.

Recent advances in single-cell technology have enabled investigation of genomic profiles and molecular crosstalk among individual cells obtained from tissues and biofluids at unprecedented resolution. Glomerular diseases, either primary or secondary to systemic diseases, often manifest elements of inflammation and of innate and adaptive immune responses. Application of single-cell methods have revealed cellular signatures of inflammation, cellular injury, and fibrosis. From these signatures, potential therapeutic targets can be inferred and in theory, this approach might facilitate identification of precision therapeutics for these diseases. Single-cell analyses of urine samples and skin lesions from patients with lupus nephritis and of urine samples from patients with diabetic nephropathy and focal segmental glomerulosclerosis have presented potential novel approaches for the diagnosis and monitoring of disease activity. These single-cell approaches, in contrast to kidney biopsy, are non-invasive and could be repeated multiple times as needed.

Efficient and precise single-cell reference atlas mapping with Symphony

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony (https://github.com/immunogenomics/symphony), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

A bird's eye view of fibroblast heterogeneity: A pan-disease, pan-cancer perspective.

Fibroblasts, custodians of tissue architecture and function, are no longer considered a monolithic entity across tissues and disease indications. Recent advances in single-cell technologies provide an unrestricted, high-resolution view of fibroblast heterogeneity that exists within and across tissues. In this review, we summarize a compendium of single-cell transcriptomic studies and provide a comprehensive accounting of fibroblast subsets, many of which have been described to occupy specific niches in tissues at homeostatic and pathologic states. Understanding this heterogeneity is particularly important in the context of cancer, as the diverse cancer-associated fibroblast (CAF) phenotypes in the tumor microenvironment (TME) are directly impacted by the expression phenotypes of their predecessors. Relationships between these heterogeneous populations often accompany and influence response to therapy in cancer and fibrosis. We further highlight the importance of integrating single-cell studies to deduce common fibroblast phenotypes across disease states, which will facilitate the identification of common signaling pathways, gene regulatory programs, and cell surface markers that are going to advance drug discovery and targeting.

Single-cell advances in stromal-leukocyte interactions in cancer.

Mesenchymal stromal cells in solid tumors have emerged as important mediators of immune function and response to immunotherapies. As such, comprehensive insights into their biology may reveal new predictors of drug response and new drug targets. While our understanding of mesenchymal biology in cancer is nascent, it is rapidly evolving, driven by advances in single-cell technologies. These studies reveal distinct subclasses of cancer-associated fibroblasts (CAFs) with unique properties for immune regulation and control of leukocyte activity. While these studies have revealed several similarities across distinct types of cancer, they still face key challenges in nomenclature. Single-cell analysis of tumors has also revealed an abundance of perivascular cells with unique biology and associations with immune infiltration. They are often misclassified, likely confounding previous bulk studies, revealing a distinct lineage of cells that remain to be fully characterized. These studies have also shed light on the discrete cell types or transient cell states that shape mesenchymal heterogeneity in tumors, offering insights into new therapeutic strategies to modulate stromal cell differentiation. In this review, we will address how recent advances in single-cell technologies have shaped our understanding of stromal heterogeneity and their coordination of immune responses in cancer.

Simultaneous deep generative modeling and clustering of single cell genomic data.

Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we proposed scDEC, a computational tool for single cell ATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks (GANs), and is capable of learning the latent representation and inferring the cell labels, simultaneously. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In downstream applications, we demonstrated that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations. We also showed that it is possible to extend scDEC for the integrative analysis of multi-modal single cell data.

Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data.

Recent advances in single-cell technologies enable spatial expression profiling at the cell level, making it possible to elucidate spatial changes of cell-specific genomic features. The gene co-expression network is an important feature that encodes the gene-gene marginal dependence structure and allows for the functional annotation of highly connected genes. In this paper, we design a simple and computationally efficient two-step algorithm to recover spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data. The algorithm first estimates the gene expression covariance matrix for each cell type and then leverages the spatial locations of cells to construct cell-specific networks. The second step uses expression covariance matrices estimated in step one and label information from neighboring cells as an empirical prior to obtain thresholded Bayesian posterior estimates. After completing estimates for each cell, this algorithm can further predict or interpolate gene co-expression networks on tissue positions where cells are not captured. In the simulation study, the comparison against the traditional cell-type-specific network algorithms and the cell-specific network method but without incorporating spatial information highlights the advantages of the proposed algorithm in estimation accuracy. We also applied our algorithm to real-world datasets and found some meaningful biological results. The accompanied software is available on https://github.com/jingeyu/CSSN.