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Clonally established tumor cell lines often do not recapitulate the behavior of cells in tumors. The sequencing of a whole tumor tissue may not uncover transcriptome profiles induced by the interactions of all different cell types within a tumor. Interferons for instance have a vast number of binding sites in their target genes. Access to the DNA binding sites is determined by the epigenomic state of each different cell type within a tumor mass. To understand how genes such as interferons appear to have both tumor-promoting and tumor-inhibiting functions, single-cell transcript analysis was performed in the breast cancer tissue of HER2+ (epidermal growth factor receptor 2) patients. We identified that potential antagonistic oncogenic activities of cells can be due to diverse expression patterns of genes with pleiotropic functions. Molecular pathways both known and novel were identified and were similar with those previously identified for patients with rheumatoid arthritis. Our study demonstrates the efficacy in using single-cell transcript analysis to gain insight into genes with apparent contradictory or paradoxical roles in oncogenesis.

RNA sequencing samples the entire transcriptome but lacks anatomical information. In situ hybridization, on the other hand, can only profile a small number of transcripts. In situ sequencing technologies address these shortcomings but face a challenge in dense, complex tissue environments. Wang et al. combined an efficient sequencing approach with hydrogel-tissue chemistry to develop a multidisciplinary technology for three-dimensional (3D) intact-tissue RNA sequencing (see the Perspective by Knöpfel). More than 1000 genes were simultaneously mapped in sections of mouse brain at single-cell resolution to define cell types and circuit states and to reveal cell organization principles. Science , this issue p. eaat5691 ; see also p. 328 Wang et al . describe the development and application of an RNA sequencing technology to define cell types and circuit states in the mouse brain. Retrieving high-content gene-expression information while retaining three-dimensional (3D) positional anatomy at cellular resolution has been difficult, limiting integrative understanding of structure and function in complex biological tissues. We developed and applied a technology for 3D intact-tissue RNA sequencing, termed STARmap (spatially-resolved transcript amplicon readout mapping), which integrates hydrogel-tissue chemistry, targeted signal amplification, and in situ sequencing. The capabilities of STARmap were tested by mapping 160 to 1020 genes simultaneously in sections of mouse brain at single-cell resolution with high efficiency, accuracy, and reproducibility. Moving to thick tissue blocks, we observed a molecularly defined gradient distribution of excitatory-neuron subtypes across cubic millimeter–scale volumes (>30,000 cells) and a short-range 3D self-clustering in many inhibitory-neuron subtypes that could be identified and described with 3D STARmap.

Understanding the spatial organization of gene expression with single-nucleotide resolution requires localizing the sequences of expressed RNA transcripts within a cell in situ. Here, we describe fluorescent in situ RNA sequencing (FISSEQ), in which stably cross-linked complementary DNA (cDNA) amplicons are sequenced within a biological sample. Using 30-base reads from 8102 genes in situ, we examined RNA expression and localization in human primary fibroblasts with a simulated wound-healing assay. FISSEQ is compatible with tissue sections and whole-mount embryos and reduces the limitations of optical resolution and noisy signals on single-molecule detection. Our platform enables massively parallel detection of genetic elements, including gene transcripts and molecular barcodes, and can be used to investigate cellular phenotype, gene regulation, and environment in situ.

Single-cell RNA-sequencing (scRNA-seq) provides high-resolution insights into complex tissues. Cardiac tissue, however, poses a major challenge due to the delicate isolation process and the large size of mature cardiomyocytes. Regardless of the experimental technique, captured cells are often impaired and some capture sites may contain multiple or no cells at all. All this refers to “low quality” potentially leading to data misinterpretation. Common standard quality control parameters involve the number of detected genes, transcripts per cell, and the fraction of transcripts from mitochondrial genes. While cutoffs for transcripts and genes per cell are usually user-defined for each experiment or individually calculated, a fixed threshold of 5% mitochondrial transcripts is standard and often set as default in scRNA-seq software. However, this parameter is highly dependent on the tissue type. In the heart, mitochondrial transcripts comprise almost 30% of total mRNA due to high energy demands. Here, we demonstrate that a 5%-threshold not only causes an unacceptable exclusion of cardiomyocytes but also introduces a bias that particularly discriminates pacemaker cells. This effect is apparent for our in vitro generated induced-sinoatrial-bodies (iSABs; highly enriched physiologically functional pacemaker cells), and also evident in a public data set of cells isolated from embryonal murine sinoatrial node tissue (Goodyer William et al. in Circ Res 125:379–397, 2019). Taken together, we recommend omitting this filtering parameter for scRNA-seq in cardiovascular applications whenever possible.

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates using relatively homogeneous cell populations. However, to determine these rates during complex dynamical processes, for instance during cellular differentiation, single-cell resolution is required. Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory strategies. These strategies have distinct consequences for controlling the dynamic range and precision of gene expression. These findings advance our understanding of how individual cells in heterogeneous populations shape their gene expression dynamics.

In multicellular organisms, the entry into meiosis is a complex process characterized by increasing meiotic specialization. Using single-cell RNA sequencing, we reconstructed the developmental program into maize male meiosis. A smooth continuum of expression stages before meiosis was followed by a two-step transcriptome reorganization in leptotene, during which 26.7% of transcripts changed in abundance by twofold or more. Analysis of cell-cycle gene expression indicated that nearly all pregerminal cells proliferate, eliminating a stem-cell model to generate meiotic cells. Mutants defective in somatic differentiation or meiotic commitment expressed transcripts normally present in early meiosis after a delay; thus, the germinal transcriptional program is cell autonomous and can proceed despite meiotic failure.

Current single-cell RNA-seq approaches are hindered by preamplification bias, loss of strand of origin information, and the inability to observe small-RNA and mRNA dual transcriptomes. Here, we introduce a single-cell holo-transcriptome sequencing (Holo-Seq) that overcomes all three hurdles. Holo-Seq has the same quantitative accuracy and uniform coverage with a complete strand of origin information as bulk RNA-seq. Most importantly, Holo-Seq can simultaneously observe small RNAs and mRNAs in a single cell. Furthermore, we acquire small RNA and mRNA dual transcriptomes of 32 human hepatocellular carcinoma single cells, which display the genome-wide super-enhancer activity and hepatic neoplasm kinetics of these cells.

Efficacy of immunotherapy in hepatocellular carcinoma (HCC) is blocked by its high degree of inter‐ and intra‐tumor heterogeneity and immunosuppressive tumor microenvironment. However, the correlation between tumor heterogeneity and immunosuppressive microenvironment in HCC has not been well addressed. Here, we endeavored to dissect inter‐ and intra‐tumor heterogeneity in HCC and uncover how they contribute to the immunosuppressive microenvironment. We performed consensus molecular subtyping with non‐negative matrix factorization (NMF) clustering to stratify the inter‐heterogeneity profile of HCC tumors. We grouped HCC tumors from the Cancer Genome Atlas (TCGA) patients into three subtypes (S1, S2 and S3), where S1 was characterized as a ‘hot tumor’ profile with high expression level of T cell genes and rate of immune scores. S2 was characterized as a ‘cold tumor’ profile with the highest tumor purity score, and S3 as an ‘immunosuppressed tumor’ profile with the poorest prognosis and a high expression level of immunosuppressive genes such as cytotoxic T‐lymphocyte‐associated protein‐4, TIGIT, and PDCD1. Moreover, we combined weighted gene co‐expression network analysis and single‐cell regulatory network inference and clustering (SCENIC) in the single‐cell dataset of the S3‐like subtype (CS3) and identified a transcription factor, BATF, which could upregulate immunosuppressive genes. Finally, we identified a cell interaction network in which a myeloid‐derived suppressor cell‐like macrophage subtype could promote the formation of immunosuppressive T‐cells. By integrating bulk and single‐cell RNA sequencing in this study, we defined a 108‐gene classifier and investigated cellular heterogeneity, as well as immune infiltration in hepatocellular carcinoma (HCC). Patients with a high level of cytotoxic T‐lymphocyte‐associated protein‐4, TIGIT and LAG3 showed the poorest prognosis in both the Cancer Genome Atlas and International Cancer Genome Consortium cohorts. Additionally, single‐cell RNA sequencing analysis revealed high expression levels of these immunosuppressive genes and poor patient survival within the CS3 cluster. Our research indicated that BATF could be regulating the expression of immunosuppressive genes in HCC.

Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3′ tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.

Cell‐to‐cell variability in gene expression is important for many processes in biology, including embryonic development and stem cell homeostasis. While heterogeneity of gene expression levels has been extensively studied, less attention has been paid to mRNA polyadenylation isoform choice. 3′ untranslated regions regulate mRNA fate, and their choice is tightly controlled during development, but how 3′ isoform usage varies within genetically and developmentally homogeneous cell populations has not been explored. Here, we perform genome‐wide quantification of polyadenylation site usage in single mouse embryonic and neural stem cells using a novel single‐cell transcriptomic method, BATSeq. By applying BATBayes, a statistical framework for analyzing single‐cell isoform data, we find that while the developmental state of the cell globally determines isoform usage, single cells from the same state differ in the choice of isoforms. Notably this variation exceeds random selection with equal preference in all cells, a finding that was confirmed by RNA FISH data. Variability in 3′ isoform choice has potential implications on functional cell‐to‐cell heterogeneity as well as utility in resolving cell populations. Synopsis BATSeq, the first transcriptomic method to quantify polyadenylation site use in single cells, reveals that stem cells from homogeneous populations differ in their preference for 3′ mRNA isoforms. We introduce BATBayes, a Bayesian framework that accounts for technical and biological sources of noise to quantify underlying variability in isoform preference 3′ isoform usage is sufficient to distinguish between cells from different stem cell populations Within homogeneous cell populations, cells differ in their use of isoforms more than expected from random choice An intrinsic mechanism acting at the level of individual genes is likely to contribute to isoform choice variability Graphical Abstract BATSeq, the first transcriptomic method to quantify polyadenylation site use in single cells, reveals that stem cells from homogeneous populations differ in their preference for 3′ mRNA isoforms.