Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment.

The development of CD4+ T cells and CD8+ T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-peptide major histocompatibility complex (MHC) class I or II by the T cell antigen receptor (TCR) determines the CD8+ or CD4+ T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell states for each T cell lineage. These analyses identified a sequential process whereby all thymocytes initiate CD4+ T cell lineage differentiation during a first wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8+ T cell lineage specification. CITE-seq and pharmaceutical inhibition experiments implicated a TCR-calcineurin-NFAT-GATA3 axis in driving the CD4+ T cell fate. Our data provide a resource for understanding cell fate decisions and implicate a sequential selection process in guiding lineage choice.

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.

Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages.

Single-cell RNA sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue, for which collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nucleus extraction is often much more efficient and therefore single-nucleus RNA sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We show that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, as well as how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we show successful integration of scRNA-seq and snRNA-seq data sets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for the characterization of cellular diversity in the adipose tissue.

DiMeLo-seq: a long-read, single-molecule method for mapping protein-DNA interactions genome wide.

Studies of genome regulation routinely use high-throughput DNA sequencing approaches to determine where specific proteins interact with DNA, and they rely on DNA amplification and short-read sequencing, limiting their quantitative application in complex genomic regions. To address these limitations, we developed directed methylation with long-read sequencing (DiMeLo-seq), which uses antibody-tethered enzymes to methylate DNA near a target protein's binding sites in situ. These exogenous methylation marks are then detected simultaneously with endogenous CpG methylation on unamplified DNA using long-read, single-molecule sequencing technologies. We optimized and benchmarked DiMeLo-seq by mapping chromatin-binding proteins and histone modifications across the human genome. Furthermore, we identified where centromere protein A localizes within highly repetitive regions that were unmappable with short sequencing reads, and we estimated the density of centromere protein A molecules along single chromatin fibers. DiMeLo-seq is a versatile method that provides multimodal, genome-wide information for investigating protein-DNA interactions.

Histologically resolved multiomics enables precise molecular profiling of human intratumor heterogeneity.

Both the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multiomics in unfixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser microdissection of regions of interests. These microtissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human oral squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multiomics assessments.

Joint probabilistic modeling of single-cell multi-omic data with totalVI.

The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.

High-throughput smFRET analysis of freely diffusing nucleic acid molecules and associated proteins.

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique for nanometer-scale studies of single molecules. Solution-based smFRET, in particular, can be used to study equilibrium intra- and intermolecular conformations, binding/unbinding events and conformational changes under biologically relevant conditions without ensemble averaging. However, single-spot smFRET measurements in solution are slow. Here, we detail a high-throughput smFRET approach that extends the traditional single-spot confocal geometry to a multispot one. The excitation spots are optically conjugated to two custom silicon single photon avalanche diode (SPAD) arrays. Two-color excitation is implemented using a periodic acceptor excitation (PAX), allowing distinguishing between singly- and doubly-labeled molecules. We demonstrate the ability of this setup to rapidly and accurately determine FRET efficiencies and population stoichiometries by pooling the data collected independently from the multiple spots. We also show how the high throughput of this approach can be used o increase the temporal resolution of single-molecule FRET population characterization from minutes to seconds. Combined with microfluidics, this high-throughput approach will enable simple real-time kinetic studies as well as powerful molecular screening applications.

Quantitative imaging of lipid droplets in single cells.

The combination of next generation sequencing (NGS) and automated liquid handling platforms has led to a revolution in single-cell genomic studies. However, many molecules that are critical to understanding the functional roles of cells in a complex tissue or organs, are not directly encoded in the genome, and therefore cannot be profiled with NGS. Lipids, for example, play a critical role in many metabolic processes but cannot be detected by sequencing. Recent developments in quantitative imaging, particularly coherent Raman scattering (CRS) techniques, have produced a suite of tools for studying lipid content in single cells. This article reviews CRS imaging and computational image processing techniques for non-destructive profiling of dynamic changes in lipid composition and spatial distribution at the single-cell level. As quantitative CRS imaging progresses synergistically with microfluidic and microscopic platforms for single-cell genomic analysis, we anticipate that these techniques will bring researchers closer towards combined lipidomic and genomic analysis.

Ultralarge Modulation of Fluorescence by Neuromodulators in Carbon Nanotubes Functionalized with Self-Assembled Oligonucleotide Rings.

Noncovalent interactions between single-stranded DNA (ssDNA) oligonucleotides and single wall carbon nanotubes (SWNTs) have provided a unique class of tunable chemistries for a variety of applications. However, mechanistic insight into both the photophysical and intermolecular phenomena underlying their utility is lacking, which results in obligate heuristic approaches for producing ssDNA-SWNT based technologies. In this work, we present an ultrasensitive "turn-on" nanosensor for neuromodulators dopamine and norepinephrine with strong relative change in fluorescence intensity (Δ F/ F0) of up to 3500%, a signal appropriate for in vivo neuroimaging, and uncover the photophysical principles and intermolecular interactions that govern the molecular recognition and fluorescence modulation of this nanosensor synthesized from the spontaneous self-assembly of (GT)6 ssDNA rings on SWNTs. The fluorescence modulation of the ssDNA-SWNT conjugate is shown to exhibit remarkable sensitivity to the ssDNA sequence chemistry, length, and surface density, providing a set of parameters with which to tune nanosensor dynamic range, analyte selectivity and strength of fluorescence turn-on. We employ classical and quantum mechanical molecular dynamics simulations to rationalize our experimental findings. Calculations show that (GT)6 ssDNA form ordered rings around (9,4) SWNTs, inducing periodic surface potentials that modulate exciton recombination lifetimes. Further evidence is presented to elucidate how dopamine analyte binding modulates SWNT fluorescence. We discuss the implications of our findings for SWNT-based molecular imaging applications.

Microfluidic single-cell whole-transcriptome sequencing.

Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole-transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2 M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis.

Label-Free Digital Quantification of Lipid Droplets in Single Cells by Stimulated Raman Microscopy on a Microfluidic Platform.

Quantitative characterization of a single-cell phenotype remains challenging. We combined a scalable microfluidic array of parallel cell culture chambers and stimulated Raman scattering (SRS) microscopy to quantitatively characterize the response of lipid droplet (LD) formation to free-fatty-acid stimuli with single-LD resolution at the single-cell level. By enabling the systematic live-cell imaging with SRS microscopy in a microfluidic device, we were able to quantify the morphology of over a thousand live cells in 10 different chemical environments and with 8 replicates for each culture condition, in a single experiment, and without relying on fluorescent labeling. We developed an image processing pipeline for cell segmentation and LD morphology quantification using dual-channel SRS images. This allows us to construct distributions of the morphological parameters of LDs in the cellular population and expose the vast phenotypic heterogeneity among genetically similar cells. Specifically, this approach provides an analytical tool for quantitatively investigating LD morphology in live cells in situ. With this high-throughput, high-resolution, and label-free method, we found that LD growth dynamics showed considerable cell to cell variation. Lipid accumulation in nonadipocyte cells is mainly reflected in the increase of LD number, as opposed to an increase in their size or lipid concentration. Our method allows statistical single-cell quantification of the LD distribution for further investigation of lipid metabolism and dynamic behavior, and also extends the possibility to couple with other "omics" technologies in the future.

Imaging without fluorescence: nonlinear optical microscopy for quantitative cellular imaging.

Quantitative single-cell analysis enables the characterization of cellular systems with a level of detail that cannot be achieved with ensemble measurement. In this Feature we explore quantitative cellular imaging applications with nonlinear microscopy techniques. We first offer an introductory tutorial on nonlinear optical processes and then survey a range of techniques that have proven to be useful for quantitative live cell imaging without fluorescent labels.

High-throughput single-molecule optofluidic analysis.

We describe a high-throughput, automated single-molecule measurement system, equipped with microfluidics. The microfluidic mixing device has integrated valves and pumps to accurately accomplish titration of biomolecules with picoliter resolution. We demonstrate that the approach enabled rapid sampling of biomolecule conformational landscape and of enzymatic activity, in the form of transcription by Escherichia coli RNA polymerase, as a function of the chemical environment.

Towards an active droplet-based microfluidic platform for programmable fluid handling.

Droplet-based microfluidic systems have emerged as powerful alternatives to conventional high throughput screening platforms, due to their operational flexibility, high-throughput nature and ability to efficiently process small fluid volumes. However, the challenges associated with performing bespoke operations on user-defined droplets often limit their utility in screening applications that involve complex workflows. To this end, the marriage of droplet- and valve-based microfluidic technologies offers the prospect of balancing the controllability of droplet manipulations and analytical throughput. In this spirit, we present a microfluidic platform that combines the capabilities of integrated microvalve technology with droplet-based sample compartmentalization to realize a highly adaptable programmable fluid handling functionality. The microfluidic device consists of a programmable formulator linked to an automated droplet generation device and storage array. The formulator leverages multiple inputs coupled to a mixing ring to produce combinatorial solution mixtures, with a peristaltic pump enabling titration of reagents into the ring with picoliter resolution. The platform allows for the execution of user-defined reaction protocols within an array of storage chambers by consecutively merging programmable sequences of pL-volume droplets containing specified reagents. The precision in formulating solutions with small differences in concentration is perfectly suited for the accurate estimation of kinetic parameters. The utility of our platform is showcased through the performance of enzymatic kinetic measurements of beta-galactosidase and horseradish peroxidase with fluorogenic substrates. The presented platform provides for a range of automated manipulations and paves the way for a more diverse range of droplet-based biological experiments.

Mapping the transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq.

Adipogenesis is key to maintaining organism-wide energy balance and healthy metabolic phenotype, making it critical to thoroughly comprehend its molecular regulation in humans. By single-nuclei RNA-sequencing (snRNA-seq) of over 20,000 differentiating white and brown preadipocytes, we constructed a high-resolution temporal transcriptional landscape of human white and brown adipogenesis. White and brown preadipocytes were isolated from a single individual's neck region, thereby eliminating inter-subject variability across two distinct lineages. These preadipocytes were also immortalized to allow for controlled, in vitro differentiation, allowing sampling of distinct cellular states across the spectrum of adipogenic progression. Pseudotemporal cellular ordering revealed the dynamics of ECM remodeling during early adipogenesis, and lipogenic/thermogenic response during late white/brown adipogenesis. Comparison with adipogenic regulation in murine models Identified several novel transcription factors as potential targets for adipogenic/thermogenic drivers in humans. Among these novel candidates, we explored the role of TRPS1 in adipocyte differentiation and showed that its knockdown impairs white adipogenesis in vitro. Key adipogenic and lipogenic markers revealed in our analysis were applied to analyze publicly available scRNA-seq datasets; these confirmed unique cell maturation features in recently discovered murine preadipocytes, and revealed inhibition of adipogenic expansion in humans with obesity. Overall, our study presents a comprehensive molecular description of both white and brown adipogenesis in humans and provides an important resource for future studies of adipose tissue development and function in both health and metabolic disease state.

Loss of TJP1 disrupts gastrulation patterning and increases differentiation toward the germ cell lineage in human pluripotent stem cells.

Biological patterning events that occur early in development establish proper tissue morphogenesis. Identifying the mechanisms that guide these patterning events is necessary in order to understand the molecular drivers of development and disease and to build tissues in vitro. In this study, we use an in vitro model of gastrulation to study the role of tight junctions and apical/basolateral polarity in modulating bone morphogenic protein-4 (BMP4) signaling and gastrulation-associated patterning in colonies of human pluripotent stem cells (hPSCs). Disrupting tight junctions via knockdown (KD) of the scaffolding tight junction protein-1 (TJP1, also known as ZO1) allows BMP4 to robustly and ubiquitously activate pSMAD1/5 signaling over time, resulting in loss of the patterning phenotype and marked differentiation bias of pluripotent stem cells to primordial germ cell-like cells (PGCLCs). These findings give important insights into how signaling events are regulated and lead to spatial emergence of diverse cell types in vitro.

Phototoxic effects of nonlinear optical microscopy on cell cycle, oxidative states, and gene expression.

Nonlinear optical imaging modalities, such as stimulated Raman scattering (SRS) microscopy, use pulsed-laser excitation with high peak intensity that can perturb the native state of cells. In this study, we used bulk RNA sequencing, quantitative measurement of cell proliferation, and fluorescent measurement of the generation of reactive oxygen species to assess phototoxic effects of near-IR pulsed laser radiation, at different time scales, for laser excitation settings relevant to SRS imaging. We define a range of laser excitation settings for which there was no significant change in mouse Neuro2A cells after laser exposure. This study provides guidance for imaging parameters that minimize photo-induced perturbations in SRS microscopy to ensure accurate interpretation of experiments with time-lapse imaging or with paired measurements of imaging and sequencing on the same cells.

Mechanical phenotyping reveals unique biomechanical responses in retinoic acid-resistant acute promyelocytic leukemia.

All-trans retinoic acid (ATRA) is an essential therapy in the treatment of acute promyelocytic leukemia (APL), but nearly 20% of patients with APL are resistant to ATRA. As there are no biomarkers for ATRA resistance that yet exist, we investigated whether cell mechanics could be associated with this pathological phenotype. Using mechano-node-pore sensing, a single-cell mechanical phenotyping platform, and patient-derived APL cell lines, we discovered that ATRA-resistant APL cells are less mechanically pliable. By investigating how different subcellular components of APL cells contribute to whole-cell mechanical phenotype, we determined that nuclear mechanics strongly influence an APL cell's mechanical response. Moreover, decondensing chromatin with trichostatin A is especially effective in softening ATRA-resistant APL cells. RNA-seq allowed us to compare the transcriptomic differences between ATRA-resistant and ATRA-responsive APL cells and highlighted gene expression changes that could be associated with mechanical changes. Overall, we have demonstrated the potential of "physical" biomarkers in identifying APL resistance.

CXCR3 regulates stem and proliferative CD8+ T cells during chronic infection by promoting interactions with DCs in splenic bridging channels.

Production of effector CD8+ T cells during persistent infection requires a stable pool of stem-like cells that can give rise to effector cells via a proliferative intermediate population. In infection models marked by T cell exhaustion, this process can be transiently induced by checkpoint blockade but occurs spontaneously in mice chronically infected with the protozoan intracellular parasite Toxoplasma gondii. We observe distinct locations for parasite-specific T cell subsets, implying a link between differentiation and anatomical niches in the spleen. Loss of the chemokine receptor CXCR3 on T cells does not prevent white pulp-to-red pulp migration but reduces interactions with CXCR3 ligand-producing dendritic cells (DCs) and impairs memory-to-intermediate transition, leading to a buildup of memory T cells in the red pulp. Thus, CXCR3 increases T cell exposure to differentiation-inducing signals during red pulp migration, providing a dynamic mechanism for modulating effector differentiation in response to environmental signals.

Complete genomic and epigenetic maps of human centromeres.

Existing human genome assemblies have almost entirely excluded repetitive sequences within and near centromeres, limiting our understanding of their organization, evolution, and functions, which include facilitating proper chromosome segregation. Now, a complete, telomere-to-telomere human genome assembly (T2T-CHM13) has enabled us to comprehensively characterize pericentromeric and centromeric repeats, which constitute 6.2% of the genome (189.9 megabases). Detailed maps of these regions revealed multimegabase structural rearrangements, including in active centromeric repeat arrays. Analysis of centromere-associated sequences uncovered a strong relationship between the position of the centromere and the evolution of the surrounding DNA through layered repeat expansions. Furthermore, comparisons of chromosome X centromeres across a diverse panel of individuals illuminated high degrees of structural, epigenetic, and sequence variation in these complex and rapidly evolving regions.