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This paper provides a laboratory workflow for single-nucleus RNA-sequencing (snRNA-seq) including a protocol for gentle nuclei isolation from fresh frozen tumor biopsies, making it possible to analyze biobanked material. To develop this protocol, we used non-frozen and frozen human bladder tumors and cell lines. We tested different lysis buffers (IgePal and Nuclei EZ) and incubation times in combination with different approaches for tissue and cell dissection; sectioning, semi-automated dissociation, manual dissociation with pestles, and semi-automated dissociation combined with manual dissociation with pestles. Our results showed a combination of IgePal lysis buffer, tissue dissection by sectioning and short incubation time was the best conditions for gentle nuclei isolation applicable for snRNA-seq, and we found limited confounding transcriptomic changes based on the isolation procedure. This protocol makes it possible to analyze biobanked material from patients with well described clinical and histopathological information and known clinical outcomes with snRNA-seq.

Immune checkpoint inhibitors (ICIs) have become a key therapeutic modality for advanced non-small cell lung cancer (NSCLC), but most patients experience primary or acquired resistance to these drugs. We here explored the mechanisms underlying both types of ICI resistance by analysis of the tumor immune microenvironment (TME).Four patients who experienced a long-term response to ICI treatment (progression-free survival [PFS] of ≥12 months) followed by disease progression, after which a rebiopsy was immediately performed (cohort-A), as well as four patients who experienced early tumor progression during ICI treatment (PFS of9 weeks, cohort-B) were enrolled in this retrospective study. The pretreatment TME was evaluated by 16- or 17-color multiplex immunohistochemistry (mIHC)-based spatial profiling at the single-cell level for both cohorts. In cohort-A, changes in the TME after disease progression during ICI treatment were also investigated by mIHC analysis and transcriptomic analysis.Pretreatment tumor tissue from cohort-B manifested poor infiltration of tumor-reactive CD8The presence of intratumoral tumor-reactive CD8

Cellular heterogeneity is fundamental to both developmental differentiation and disease establishment. Recent advances in high-throughput single-cell technology have been rapidly revolutionizing the resolution of our understanding of development and disease. However, while the study of single-cell transcriptomes is easily accessible, the analysis of single-cell proteomes is still in its infancy. In this study, we describe simultaneous profiling of multiple regulatory proteins at a single-cell level using mass cytometry or cytometry by time of flight. We develop mass cytometry reagents to study key transcription factors, signaling proteins and chromatin modifiers that regulate mouse embryonic stem cells. Our data reveal that the protein level of stem cell regulators significantly varies and that cell signaling pathways are extensively cross-activated across defined culture conditions of embryonic stem cells. In addition, the mass cytometry data enabled us to identify distinct multiple cell states of embryonic stem cells and determine their variation across culture conditions. We discuss the mass cytometry method, our results of the multi-protein analysis in embryonic stem cells and potential future perspectives for single-cell protein analysis.

The accumulation of senescent cells is a major cause of age-related inflammation and predisposes to a variety of age-related diseasessup1/sup. However, little is known about the molecular basis underlying this accumulation and its potential as a target to ameliorate the ageing process. Here we show that senescent cells heterogeneously express the immune checkpoint protein programmed death-ligand 1 (PD-L1) and that PD-L1sup+/supsenescent cells accumulate with age in vivo. PD-L1sup-/supcells are sensitive to T cell surveillance, whereas PD-L1sup+/supcells are resistant, even in the presence of senescence-associated secretory phenotypes (SASP). Single-cell analysis of p16sup+/supcells in vivo revealed that PD-L1 expression correlated with higher levels of SASP. Consistent with this, administration of programmed cell death protein 1 (PD-1) antibody to naturally ageing mice or a mouse model with normal livers or induced nonalcoholic steatohepatitis reduces the total number of p16sup+/supcells in vivo as well as the PD-L1sup+/suppopulation in an activated CD8sup+/supT cell-dependent manner, ameliorating various ageing-related phenotypes. These results suggest that the heterogeneous expression of PD-L1 has an important role in the accumulation of senescent cells and inflammation associated with ageing, and the elimination of PD-L1sup+/supsenescent cells by immune checkpoint blockade may be a promising strategy for anti-ageing therapy.

Multiscale analysis of morphogenesis requires to follow and measure in real-time the

Immune responses that result in asthma exacerbation are associated with allergen or viral exposure. Identification of common immune factors will be beneficial for the development of uniformed targeted therapy. We employed a House Dust Mite (HDM) mouse model of asthma and challenged allergic HDM mice with allergens (HDM, cockroach extract (CRE)) or respiratory syncytial virus (RSV). Purified lung immune cells underwent high-dimensional single-cell RNA deep sequencing (scRNA-seq) to generate an RNA transcriptome. Gene silencing with siRNA was employed to confirm the efficacy of scRNA-seq analysis. scRNA-seq UMAP analysis portrayed an array of cell markers within individual immune clusters. SCENIC R analysis showed an increase in regulon number and activity in CD11b

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

The landscape and function of the immune infiltrate of intrahepatic cholangiocarcinoma (iCCA), a rare, yet aggressive tumor of the biliary tract, remains poorly characterized, limiting development of successful immunotherapies. Herein, we aimed to define the molecular characteristics of tumor-infiltrating leukocytes with a special focus on CD4+ regulatory T cells (Tregs).We used high-dimensional single-cell technologies to characterize the T-cell and myeloid compartments of iCCA tissues, comparing these with their tumor-free peritumoral and circulating counterparts. We further used genomics and cellular assays to define the iCCA-specific role of a novel transcription factor, mesenchyme homeobox 1 (MEOX1), in Treg biology.We found poor infiltration of putative tumor-specific CD39+ CD8+ T cells accompanied by abundant infiltration of hyperactivated CD4+ Tregs. Single-cell RNA-sequencing identified an altered network of transcription factors in iCCA-infiltrating compared to peritumoral T cells, suggesting reduced effector functions by tumor-infiltrating CD8+ T cells and enhanced immunosuppression by CD4+ Tregs. Specifically, we found that expression of MEOX1 was highly enriched in tumor-infiltrating Tregs, and demonstrated that MEOX1 overexpression is sufficient to reprogram circulating Tregs to acquire the transcriptional and epigenetic landscape of tumor-infiltrating Tregs. Accordingly, enrichment of the MEOX1-dependent gene program in Tregs was strongly associated with poor prognosis in a large cohort of patients with iCCA.We observed abundant infiltration of hyperactivated CD4+ Tregs in iCCA tumors along with reduced CD8+ T-cell effector functions. Interfering with hyperactivated Tregs should be explored as an approach to enhance antitumor immunity in iCCA.Immune cells have the potential to slow or halt the progression of tumors. However, some tumors, such as intrahepatic cholangiocarcinoma, are associated with very limited immune responses (and infiltration of cancer-targeting immune cells). Herein, we show that a specific population of regulatory T cells (a type of immune cell that actually suppresses the immune response) are hyperactivated in intrahepatic cholangiocarcinoma. Targeting these cells could enable cancer-targeting immune cells to act more effectively and should be looked at as a potential therapeutic approach to this aggressive cancer type.

UNIFAN is an unsupervised cell type annotation tool for single-cell RNA sequencing data (scRNA-seq). Given single-cell expression data as input, UNIFAN outputs cell clusters as well as annotations for each cluster. The clustering process utilizes information on pathways and biological processes and these are also used to annotate the resulting clusters. In this software article, we focus on how to install UNIFAN and on the main steps involved in using UNIFAN for cell type annotations.

International audience; Cancer-Associated Fibroblasts (CAFs) represent the most prominent component of the tumor microenvironment (TME). Recent studies demonstrated that CAF are heterogeneous and composed of different subpopulations exerting distinct functions in cancer. CAF populations differentially modulate various aspects of tumor growth, including cancer cell proliferation, extra-cellular matrix remodeling, metastatic dissemination, immunosuppression and resistance to treatment. Among other markers, the Fibroblast Activation Protein (FAP) led to the identification of a specific CAF subpopulation involved in metastatic spread and immunosuppression. Expression of FAP at the surface of CAF is detected in many different cancer types of poor prognosis. Thus, FAP recently appears as an appealing target for therapeutic and molecular imaging applications. In that context, 68 Ga-labeled radiopharmaceutical-FAP-inhibitors (FAPI) have been recently developed and validated for quantitatively mapping FAP expression over the whole-body using Positron Emission Tomography (PET/CT). In this review, we describe the main current knowledge on CAF subpopulations and their distinct functions in solid cancer, as well as the promising diagnostic and therapeutic implications of radionuclides targeting FAP.

Breast cancers are known to be driven by the transcription factor estrogen receptor and its ligand estrogen. While the receptor's cis-binding elements are known to vary between tumors, heterogeneity of hormone signaling at a single-cell level is unknown. In this study, we systematically tracked estrogen response across time at a single-cell level in multiple cell line and organoid models. To accurately model these changes, we developed a computational tool (TITAN) that quantifies signaling gradients in single-cell datasets. Using this approach, we found that gene expression response to estrogen is non-uniform, with distinct cell groups expressing divergent transcriptional networks. Pathway analysis suggested the two most distinct signatures are driven separately by ER and FOXM1. We observed that FOXM1 was indeed activated by phosphorylation upon estrogen stimulation and silencing of FOXM1 attenuated the relevant gene signature. Analysis of scRNA-seq data from patient samples confirmed the existence of these divergent cell groups, with the FOXM1 signature predominantly found in ER negative cells. Further, multi-omic single-cell experiments indicated that the different cell groups have distinct chromatin accessibility states. Our results provide a comprehensive insight into ER biology at the single-cell level and potential therapeutic strategies to mitigate resistance to therapy.

Motivation Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements. Results In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes. Availability and implementation The source code is freely available at https://github.com/zhongjiayuan/DNRS. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell–cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. Results The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell–cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. Availability and implementation scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. Results We introduce AIscEA—Alignment-based Integration of single-cell gene Expression and chromatin Accessibility—a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. Availability and implementation AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub. Supplementary information Supplementary data are available at Bioinformatics online.

Background. Sepsis can lead to multiple organ damage, of which the heart is one of the most vulnerable organs. Vagal nerve stimulation can reduce myocardial injury in sepsis and improve survival rates. However, the potential impact of low-level tragus stimulation and disparate cell populations on sepsis-induced myocardial dysfunction remains undetermined. Methods. A cardiac single-cell transcriptomic approach was used for characterizing cardiac cell populations that form the heart. Single-cell mRNA sequencing data were used for selecting all cardiac macrophages from CD45+ cells. Then, echocardiography, western blot, flow cytometry, immunofluorescence, and immunohistochemistry were performed to verify the single-cell mRNA sequencing results. Results. Using single-cell mRNA sequencing data, we uncovered the multiple cell populations contributing to myocardial injury in sepsis under low-level tragus stimulation, thereby illustrating a comprehensive map of the cardiac cellular landscape. Pseudotiming analysis in single-cell sequencing showed that low-level vagal nerve stimulation played an anti-inflammatory role by promoting cardiac monocytes into M2 macrophages, which significantly increased α7nAChR expression in heart tissues. Echocardiography assessment indicated that low-level vagal nerve stimulation could also improve cardiac functions in mice with sepsis-induced myocardial dysfunction. In addition, the heart tissues of mice from the sepsis group with low-level tragus stimulation had significantly lower interleukin-1β expression levels than those from the sepsis group. Flow cytometry analysis showed that different acetylcholine concentrations promoted cardiac monocytes into M2 macrophages in in vitro experiments. Conclusion. Low-level tragus stimulation could improve sepsis-induced myocardial dysfunction by promoting cardiac monocytes to M2 macrophages.

Motivation The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse. Results We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices. It provides a workflow consisting of imputation and embedding, graph construction, dual graph refinement, pseudotime calculation and result visualization. Beyond the few existing methods, scHiCPTR ties to optimize graph structure by two parallel procedures of graph pruning, which help reduce the spurious cell links resulted from noises and determine a global developmental directionality. Besides, it has an ability to handle developmental trajectories with multiple topologies, including linear, bifurcated and circular ones, and is competitive with methods developed for single-cell RNA-seq data. The comparative results tell that our scHiCPTR can achieve higher performance in pseudotime inference, and the inferred developmental trajectory exhibit a reasonable biological significance. Availability and implementation scHiCPTR is freely available at https://github.com/lhqxinghun/scHiCPTR. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and more well-annotated scRNA-seq reference data are publicly available, automatical label transference algorithms are gaining popularity over manual marker gene-based annotation methods. However, most existing methods fail to unify cell-type annotation with dimensionality reduction and are unable to generate deep latent representation from the perspective of data generation. Results In this article, we propose scSemiGAN, a single-cell semi-supervised cell-type annotation and dimensionality reduction framework based on a generative adversarial network, to overcome these challenges, modeling scRNA-seq data from the aspect of data generation. Our proposed scSemiGAN is capable of performing deep latent representation learning and cell-type label prediction simultaneously. Through extensive comparison with four state-of-the-art annotation methods on diverse simulated and real scRNA-seq datasets, scSemiGAN achieves competitive or superior performance in multiple downstream tasks including cell-type annotation, latent representation visualization, confounding factor removal and enrichment analysis. Availability and implementation The code and data of scSemiGAN are available on GitHub: https://github.com/rafa-nadal/scSemiGAN. Supplementary information Supplementary data are available at Bioinformatics online.

Single-cell RNA sequencing (scRNA-seq) is one of the most efficient technologies for human tumor research. However, data analysis is still faced with technical challenges, especially the difficulty in efficiently and accurately discriminating cancer/normal cells in the scRNA-seq expression matrix. If we can address these challenges, we can have a deeper understanding of the intratumoral and intertumoral heterogeneity. In this study, we developed a cancer/normal cell discrimination pipeline called pan-Cancer Seeker (CaSee) devoted to scRNA-seq expression matrix, which is based on the traditional high-quality pan-cancer bulk sequencing data using transfer learning. CaSee is the first tool directly used to discriminate cancer/normal cells in the scRNA-seq expression matrix, with much wider application fields and higher efficiency than copy number variation (CNV) method which requires corresponding reference cells. CaSee is user-friendly and can adapt to a variety of data sources, including but not limited to scRNA tissue sequencing data, scRNA cell line sequencing data, scRNA xenograft cell sequencing data and scRNA circulating tumor cell sequencing data. It is compatible with mainstream sequencing technology platforms, 10× Genomics Chromium, Smart-seq2, and Microwell-seq. Here, CaSee pipeline exhibited excellent performance in the multicenter data evaluation of 11 retrospective cohorts and one independent dataset, with an average discrimination accuracy of 96.69%. In general, the development of a deep-learning based, pan-cancer cell discrimination model, CaSee, to distinguish cancer cells from normal cells will be compelling to researchers working in the genomics, cancer, and single-cell fields.

Since Y-STR typing only amplifies male Y chromosomal DNA, it can simplify the interpretation of some DNA mixtures that contain female DNA. However, if there are multiple male contributors, mixed Y-STR DNA profiles will often be obtained. Y-STR mixture analysis cases are particularly challenging though as, currently, there are no validated probabilistic genotyping (PG) software solutions commercially available to aid in their interpretation. One approach to fully deconvoluting these challenging mixtures into their individual donors is to conduct single-cell genotyping by isolating individual cells from a mixture prior to conducting DNA typing. In this work, a physical micromanipulation technique involving a tungsten needle and direct PCR with decreased reaction volume and increased cycle number was applied to equimolar 2- and 3-person buccal cell male DNA mixtures and a mock touch DNA case scenario involving the consecutive firing of a handgun by two males. A consensus DNA profiling approach was then utilized to obtain YFiler™ Plus Y-STR haplotypes. Buccal cells were used to optimize and test the direct single-cell subsampling approach, and 2-3 person male buccal cell mixtures were fully deconvoluted into their individual donor Y-STR haplotypes. Single-cell (or agglomerated cell clump) subsampling from the gun's trigger recovered single-source Y-STR profiles from both individuals who fired the gun, the owner, and the other unrelated male. Only the non-owner's DNA was found in the cells recovered from the handle. In summary, direct single-cell subsampling as described represents a potential simple way to analyze and interpret Y-STR mixtures.

Glioma is the most common of all central nervous system (CNS) malignancies and is associated with a poor prognosis. Pyroptosis has been proven to be associated with the progression of multiple tumors and CNS diseases. However, the relationships between pyroptosis and clinical prognosis and immune cell infiltration are unclear in glioma. In this study, we conducted a comprehensive exploration of pyroptosis in glioma. First, prognosis-related genes were screened at each key regulatory locus in the pyroptosis pathway, and the prognostic ability and coexpression relationships of GSDMD and its upstream pathway genes NLRC4/CASP1/CASP4 were identified and well validated in multiple datasets. Tissue microarray-based immunohistochemistry results showed higher levels of NLRC4 and N-terminal GSDMD in high-grade gliomas, providing conclusive evidence of pyroptosis in gliomas. The robustness of the prognostic model based on these four genes was well validated in TCGA and CGGA cohorts. Bulk RNA-seq-based analysis showed that the group defined as the high-risk group according to the model showed activation of multiple inflammatory response pathways and impaired synaptic gene expression and had a higher infiltration of bone marrow-derived macrophages (BMDMs) and a hypersuppressed immune microenvironment. More importantly, three independent single-cell RNA-seq (scRNA-seq) datasets demonstrated that tumor-infiltrating macrophages, particularly BMDMs but not tissue-resident microglia, showed significant coexpression of the GSDMD and CASP genes, and BMDMs from high-grade gliomas accounted for a higher proportion of immune infiltrating cells and had higher expression of pyroptosis genes. Finally, we revealed the activation of pathways in response to LPS/bacteria and oxidative stress during BMDM development toward the pyroptosis cell fate by pseudotime trajectory analysis, suggesting potential BMDM pyroptosis initiators. The above results provide not only novel insights into the pathological mechanisms of glioma but also novel therapeutic targets for glioma, suggesting the potential application of pyroptosis inhibitors (e.g., disulfiram).

Germinal centres are the engines of antibody evolution. Here, using human immunodeficiency virus (HIV) Env protein immunogen priming in rhesus monkeys followed by a long period without further immunization, we demonstrate germinal centre B (B

The precise manipulation of single cells plays a fundamental role for single cell measurement, which is crucial for understanding the diverse cellular mechanisms. Unusual single cell behavior could thus be identified by integrating with advanced analytical methods such as single cell omics, unraveling the intrinsic cellular heterogeneity hidden in ensemble measurements. Herein, this technical note reports a nanopipet-based versatile method for manipulation of an ultrasmall volume of liquid, which further enables the precise manipulation of single cells. Femtoliter volumes of cytoplasm were extracted from single living cells and analyzed by time-of-flight secondary ion mass spectrometry. Moreover, several kinds of exogenous components were injected simultaneously into a cell, offering a delicate tool for multi-imaging in single living cells.

Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.

Motivation Unveiling the heterogeneity in the tissues is crucial to explore cell–cell interactions and cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis. Therefore, deconvolution of ST has been an indispensable step and a technical challenge toward the higher-resolution panorama of tissues. Results Here, we propose a novel ST deconvolution method called SD2 integrating spatial information of ST data and embracing an important characteristic, dropout, which is traditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-based genes as informative features from ST and scRNA-seq data by fitting a Michaelis–Menten function. After synthesizing pseudo-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover low-dimensional and non-linear representation of the real- and pseudo-ST spots. Next, we create a graph containing embedded profiles as nodes, and edges determined by transcriptional similarity and spatial relationship. Given the graph, a graph convolutional neural network is used to predict the cell-type compositions for real-ST spots. We benchmark the performance of SD2 on the simulated seqFISH+ dataset with different resolutions and measurements which show superior performance compared with the state-of-the-art methods. SD2 is further validated on three real-world datasets with different ST technologies and demonstrates the capability to localize cell-type composition accurately with quantitative evidence. Finally, ablation study is conducted to verify the contribution of different modules proposed in SD2. Availability and implementation The SD2 is freely available in github (https://github.com/leihouyeung/SD2) and Zenodo (https://doi.org/10.5281/zenodo.7024684). Supplementary information Supplementary data are available at Bioinformatics online.

Multimodal analysis of circulating tumour cells (CTCs) has the potential to provide remarkable insight for cancer development and metastasis. CTCs and CTC clusters investigation using single-cell analysis, enables researchers to gain crucial information on metastatic mechanisms and the genomic alterations responsible for drug resistance, empowering treatment, and management of cancer. Despite a plethora of CTC isolation technologies, careful attention to the strengths and weaknesses of each method should be considered in order to isolate these rare cells. Here, we provide an overview of cutting-edge technologies used for single-cell isolation and analysis of CTCs. Additionally, we highlight the biological features, clinical application, and the therapeutic potential of CTCs and CTC clusters using single-cell analysis platforms for cancer management.

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq (scRNA-seq) data. Compared with the commonly used variance-based methods, by mimicking the human maker selection in the 2D visualization of cells, a new feature selection method called HRG (Highly Regional Genes) is proposed to find the informative genes, which show regional expression patterns in the cell-cell similarity network. We mathematically find the optimal expression patterns that can maximize the proposed scoring function. In comparison with several unsupervised methods, HRG shows high accuracy and robustness, and can increase the performance of downstream cell clustering and gene correlation analysis. Also, it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

Most common methods of cellular analysis employ the top-down approach (investigating proteomics or genomics directly), thereby destroying the cell, which does not allow the possibility of using the same cell to correlate genomics with functional assays. Herein we describe an approach for single-cell tools that serve as a bottom-up approach. Our technology allows functional phenotyping to be conducted by observing the cytotoxicity of cells and then probe the underlying biology. We have developed a droplet microfluidic device capable of trapping droplets in the array and releasing the droplet of interest selectively using microvalves. Each droplet in the array encapsulates natural killer cells (NK cells) and tumour cells for real-time monitoring of burst kinetics and spatial coordination during killing by single NK cells. Finally, we use the microvalve actuation to selectively release droplets with the desired functional phenotype such as for fast and serial killing of target tumour cells by NK cells. From this perspective, our device allows for investigating first interactions and real-time monitoring of kinetics and later cell recovery on demand for single-cell omic analysis such as single-cell RNA sequencing (scRNA), which to date, is primarily based on in-depth analyses of the entire transcriptome of a relatively low number of cells.

The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.

Unsupervised cell clustering on the basis of meaningful biological variation in single-cell RNA sequencing (scRNA seq) data has received significant attention, as it assists with ontological subpopulation identification among the data. A key step in the clustering process is to compute distances between the cells under a specified distance measure. Although particular distance measures may successfully separate cells into biologically relevant clusters, they may fail to retain global structure of the data, such as relative similarity between the cell clusters. In this article, we modify a biologically motivated distance measure, SIDEseq, for use of aggregate comparisons of cell types in large single-cell assays, and demonstrate that, across simulated and real scRNA seq data, the distance matrix more consistently retains global cell type relationships than commonly used distance measures for scRNA seq clustering. We call the modified distance measure "SIDEREF." We explore spectral dimension reduction of the SIDEREF distance matrix as a means of noise filtering, similar to principal components analysis applied directly to expression data. We utilize a summary measure of relative cell type distances to better display the cell group relationships. SIDEREF visualizations more consistently reflect global structures in the data than other commonly considered distance measures. We utilize relative cell type distances and the SIDEREF distance measure to uncover compositional differences between annotated leukocyte cell groups in a compendium of

Recent advances in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) have allowed simultaneous epigenetic profiling over thousands of individual cells to dissect the cellular heterogeneity and elucidate regulatory mechanisms at the finest possible resolution. However, scATAC-seq is challenging to model computationally due to the ultra-high dimensionality, low signal-to-noise ratio, complex feature interactions, and high vulnerability to various confounding factors. In this study, we present Translator, an efficient transfer learning approach to capture generalizable chromatin interactions from high-quality (HQ) reference scATAC-seq data to obtain robust cell representations in low-to-moderate quality target scATAC-seq data. We applied Translator on various simulated and real scATAC-seq datasets and demonstrated that Translator could learn more biologically meaningful cell representations than other methods by incorporating information learned from the reference data, thus facilitating various downstream analyses such as clustering and motif enrichment measurements. Moreover, Translator's block-wise deep learning framework can handle nonlinear relationships with restricted connections using fewer parameters to boost computational efficiency through Graphics Processing Unit (GPU) parallelism. Finally, we have implemented Translator as a free software package available for the community to leverage large-scale, HQ reference data to study target scATAC-seq data.

Determining the prevalence of antimicrobial resistance (AMR) in non-clinical settings is vital for better management of the global AMR crisis. Untreated and even treated wastewaters are important sources that release AMR into the environment. Methodologically, it is difficult to generate a comprehensive in situ profile of antibiotic resistance gene hosts. Here, we used epicPCR (emulsion, paired isolation, and concatenation PCR) as a cultivation-independent method to reveal the host profiles of the AMR indicator genes intI1, sul1, sul2, and dfrA1 in two constructed wetlands treating municipal wastewater. Overall, the epicPCR analysis revealed a profile of AMR indicator gene hosts that is consistent with literature data from cultivation-based approaches. Most carriers of antibiotic resistance (AR) genes and likely of class 1 integrons belonged to the Gammaproteobateria, particularly the Burkholderiaceae and Rhodocyclaceae families, followed by members of the Campylobacterota, Desulfobacterota, and Firmicutes. The analysis also identified several novel hosts for the indicator genes widely distributed in the wetlands, including the genera Legionella and Ralstonia. Therefore, the application of epicPCR has produced an expanded insight into the in situ indicator gene host profile, while highlighting the role of the environment as a reservoir for AMR.

A powerful feature of single-cell genomics is the possibility to identify cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA-sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

Severe SARS-CoV-2 infection1 has been associated with highly inflammatory immune activation since the earliest days of the COVID-19 pandemic2–5. More recently, these responses have been associated with the emergence of self-reactive antibodies with pathologic potential6–10, although their origins and resolution have remained unclear11. Previously, we and others have identified extrafollicular B cell activation, a pathway associated with the formation of new autoreactive antibodies in chronic autoimmunity12,13, as a dominant feature of severe and critical COVID-19 (refs. 14–18). Here, using single-cell B cell repertoire analysis of patients with mild and severe disease, we identify the expansion of a naive-derived, low-mutation IgG1 population of antibody-secreting cells (ASCs) reflecting features of low selective pressure. These features correlate with progressive, broad, clinically relevant autoreactivity, particularly directed against nuclear antigens and carbamylated proteins, emerging 10–15 days after the onset of symptoms. Detailed analysis of the low-selection compartment shows a high frequency of clonotypes specific for both SARS-CoV-2 and autoantigens, including pathogenic autoantibodies against the glomerular basement membrane. We further identify the contraction of this pathway on recovery, re-establishment of tolerance standards and concomitant loss of acute-derived ASCs irrespective of antigen specificity. However, serological autoreactivity persists in a subset of patients with postacute sequelae, raising important questions as to the contribution of emerging autoreactivity to continuing symptomology on recovery. In summary, this study demonstrates the origins, breadth and resolution of autoreactivity in severe COVID-19, with implications for early intervention and the treatment of patients with post-COVID sequelae.

Nanoparticle (NP) internalization by cells is complex, highly heterogeneous, and fundamentally important for nanomedicine. We report powerful probabilistic statistics from single-cell data on quantitative NP uptake of PEG-coated transferrin receptor-targeted gold NPs for cancer-derived and fibroblast cells according to their cell size, receptor expression, and receptor density. The smaller cancer cells had a greater receptor density and more efficient uptake of targeted NPs. However, simply due to fibroblasts being larger with more receptors, they exhibited greater NP uptake. While highly heterogeneous, targeted NP uptake strongly correlated with receptor expression. When uptake was normalized to cell size, no correlation existed. Consequently, skewed population distributions in cell sizes explain the distribution in NP uptake. Furthermore, exposure to the transferrin receptor-targeted NPs alters the fibroblast size and receptor expression, suggesting that the receptor-targeted NPs may interfere with the metabolic flux and nutrient exchange, which could assist in explaining the altered regulation of cells exposed to nanoparticles. Refereed/Peer-reviewed

STUDY QUESTION What are the cellular composition and single-cell transcriptomic differences between myometrium and leiomyomas as defined by single-cell RNA sequencing? SUMMARY ANSWER We discovered cellular heterogeneity in smooth muscle cells (SMCs), fibroblast and endothelial cell populations in both myometrium and leiomyoma tissues. WHAT IS KNOWN ALREADY Previous studies have shown the presence of SMCs, fibroblasts, endothelial cells and immune cells in myometrium and leiomyomas. However, there is no information on the cellular heterogeneity in these tissues and the transcriptomic differences at the single-cell level between these tissues. STUDY DESIGN, SIZE, DURATION We collected five leiomyoma and five myometrium samples from a total of eight patients undergoing hysterectomy. We then performed single-cell RNA sequencing to generate a cell atlas for both tissues. We utilized our single-cell sequencing data to define cell types, compare cell types by tissue type (leiomyoma versus myometrium) and determine the transcriptional changes at a single-cell resolution between leiomyomas and myometrium. Additionally, we performed MED12-variant analysis at the single-cell level to determine the genotype heterogeneity within leiomyomas. PARTICIPANTS/MATERIALS, SETTING, METHODS We collected five MED12-variant positive leiomyomas and five myometrium samples from a total of eight patients. We then performed single-cell RNA sequencing on freshly isolated single-cell preparations. Histopathological assessment confirmed the identity of the samples. Sanger sequencing was performed to confirm the presence of the MED12 variant in leiomyomas. MAIN RESULTS AND ROLE OF CHANCE Our data revealed previously unknown heterogeneity in the SMC, fibroblast cell and endothelial cell populations of myometrium and leiomyomas. We discovered the presence of two different lymphatic endothelial cell populations specific to uterine leiomyomas. We showed that both myometrium and MED12-variant leiomyomas are relatively similar in cellular composition but differ in cellular transcriptomic profiles. We found that fibroblasts influence the leiomyoma microenvironment through their interactions with endothelial cells, immune cells and SMCs. Variant analysis at the single-cell level revealed the presence of both MED12 variants as well as the wild-type MED12 allele in SMCs of leiomyomatous tissue. These results indicate genotype heterogeneity of cellular composition within leiomyomas. LARGE SCALE DATA The datasets are available in the NCBI Gene Expression Omnibus (GEO) using GSE162122. LIMITATIONS, REASONS FOR CAUTION Our study focused on MED12-variant positive leiomyomas for single-cell RNA sequencing analyses. Leiomyomas carrying other genetic rearrangements may differ in their cellular composition and transcriptomic profiles. WIDER IMPLICATIONS FOR THE FINDINGS Our study provides a cellular atlas for myometrium and MED12-variant positive leiomyomas as defined by single-cell RNA sequencing. Our analysis provides significant insight into the differences between myometrium and leiomyomas at the single-cell level and reveals hitherto unknown genetic heterogeneity in multiple cell types within human leiomyomas. Our results will be important for future studies into the origin and growth of human leiomyomas. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by funding from the National Institute of Child Health and Human Development (HD098580 and HD088629). The authors declare no competing interests.

Acquired resistance to inhibitors of anaplastic lymphoma kinase (ALK) is a major clinical challenge for ALK fusion-positive non-small-cell lung cancer (NSCLC). In the absence of secondary ALK mutations, epigenetic reprogramming is one of the main mechanisms of drug resistance, as it leads to phenotype switching that occurs during the epithelial-to-mesenchymal transition (EMT). Although drug-induced epigenetic reprogramming is believed to alter the sensitivity of cancer cells to anticancer treatments, there is still much to learn about overcoming drug resistance. In this study, we used an in vitro model of ceritinib-resistant NSCLC and employed genome-wide DNA methylation analysis in combination with single-cell (sc) RNA-seq to identify cytidine deaminase (CDA), a pyrimidine salvage pathway enzyme, as a candidate drug target. CDA was hypomethylated and upregulated in ceritinib-resistant cells. CDA-overexpressing cells were rarely but definitively detected in the naïve cell population by scRNA-seq, and their abundance was increased in the acquired-resistance population. Knockdown of CDA had antiproliferative effects on resistant cells and reversed the EMT phenotype. Treatment with epigenome-related nucleosides such as 5-formyl-2′-deoxycytidine selectively ablated CDA-overexpressing resistant cells via accumulation of DNA damage. Collectively, our data suggest that targeting CDA metabolism using epigenome-related nucleosides represents a potential new therapeutic strategy for overcoming ALK inhibitor resistance in NSCLC.

Rhabdomyosarcoma (RMS) is a common childhood cancer that shares features with developing skeletal muscle. Yet, the conservation of cellular hierarchy with human muscle development and the identification of molecularly defined tumor-propagating cells has not been reported. Using single-cell RNA-sequencing, DNA-barcode cell fate mapping and functional stem cell assays, we uncovered shared tumor cell hierarchies in RMS and human muscle development. We also identified common developmental stages at which tumor cells become arrested. Fusion-negative RMS cells resemble early myogenic cells found in embryonic and fetal development, while fusion-positive RMS cells express a highly specific gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. Fusion-positive RMS cells also have neural pathway-enriched states, suggesting less-rigid adherence to muscle-lineage hierarchies. Finally, we identified a molecularly defined tumor-propagating subpopulation in fusion-negative RMS that shares remarkable similarity to bi-potent, muscle mesenchyme progenitors that can make both muscle and osteogenic cells.

A surge in research has occurred because of current developments in single-cell technologies. Above all, single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) is a popular approach of analyzing chromatin accessibility differences at the level of single cell, either within or between groups. As a result, it is critical to examine cell heterogeneity at a previously unseen level and to identify both recognized and unknown cell types. However, with the ever-increasing number of cells engendered by technological development and the characteristics of the data, such as high noise, sparsity and dimension, challenges in distinguishing cell types have emerged. We propose scVAEBGM, which integrates a Variational Autoencoder (VAE) with a Bayesian Gaussian-mixture model (BGM) to process and analyze scATAC-seq data. This method combines and takes benefits of a Bayesian Gaussian mixture model to estimate the number of cell types without determining the cluster number in a beforehand. In other words, the size of the clusters is inferred from the data, thus avoiding biases introduced by subjective assessments when manually determining the size of the clusters. Additionally, the method is more robust to noise and can better represent single-cell data in lower dimensions. We also create a further clustering strategy. It is indicated by experiments that further clustering based on the already completed clustering can improve the clustering accuracy again. We test on six public datasets, and scVAEBGM outperforms various dimension reduction baselines. In downstream applications, scVAEBGM can reveal biological cell types.

Motivation Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted. Results We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data. Availability and implementation ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4). Supplementary information Supplementary data are available at Bioinformatics online.

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.

Here, a fluorescent polymerase chain reaction (PCR) nanokit is established to detect the specific DNA sequence in a single living cell. Different from well-developed protocols to load cell-permeable probes into single cell for recognition, the DNA sequence in a cellular nucleus is sorted into a nanopipette in our strategy. The target DNA sequence is reacted with the PCR kit components in the nanopipette to complete a PCR amplification reaction. SYBR Green prefilled in the nanopipette is intercalated into double-stranded DNA to induce fluorescence emission for real-time detection down to a single copy. An obvious increase in the fluorescence is observed that validates the detection of the target DNA sequence in single living cells. The established real-time fluorescent PCR nanokit could adapt the PCR kit for single cell analysis and thus offers an alternatively general and highly sensitive strategy for the detection of specific DNA sequences in single living cells.

Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. Many molecules and signaling processes can mediate CCIs. One classical type of CCI mediator is the interaction between secreted ligands and cell surface receptors, i.e., ligand-receptor (LR) interaction. With the recent development of single-cell technologies, a large amount of single-cell RNA Sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used fifteen mouse scRNA-Seq samples that correspond to nearly 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

Immune cells have been implicated in idiopathic pulmonary fibrosis (IPF), but the phenotypes and effector mechanisms of these cells remain incompletely characterized. We performed mass cytometry to quantify immune cell subsets in lungs of 12 patients with IPF and 15 organ donors without chronic lung disease and used existing single-cell RNA-sequencing data to investigate transcriptional profiles of immune cells overrepresented in IPF. Among myeloid cells, we found increased numbers of alveolar macrophages (AMØs) and dendritic cells (DCs) in IPF, as well as a subset of monocyte-derived DCs. In contrast, monocyte-like cells and interstitial macrophages were reduced in IPF. Transcriptomic profiling identified an enrichment for IFN-γ response pathways in AMØs and DCs from IPF, as well as antigen processing in DCs and phagocytosis in AMØs. Among T cells, we identified three subsets of memory T cells that were increased in IPF, including CD4(+) and CD8(+) resident memory T cells (T(RM)) and CD8(+) effector memory cells. The response to the IFN-γ pathway was enriched in CD4 T(RM) and CD8 T(RM) cells in IPF, together with T cell activation and immune response–regulating signaling pathways. Increased AMØs, DCs, and memory T cells were present in IPF lungs compared with control subjects. In IPF, these cells possess an activation profile indicating increased IFN-γ signaling and upregulation of adaptive immunity in the lungs. Together, these studies highlight critical features of the immunopathogenesis of IPF.

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

Microbial culturing and meta-omic profiling technologies have significantly advanced our understanding of the taxonomic and functional variation of the human microbiome and its impact on host processes. The next increase in resolution will come by understanding the role of low-abundant and less-prevalent bacteria and the study of individual cell behaviors that underlie the complexity of microbial ecosystems. To this aim, single-cell techniques are being rapidly developed to isolate, culture, and characterize the genomes and transcriptomes of individual microbes in complex communities. Here, we discuss how these single-cell technologies are providing unique insights into the biology and behavior of human microbiomes.

Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell-types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences between studies or batch effects. Here, we present a statistical approach that leverages public datasets to combine information across thousands of genes, uses a latent variable model to define cell-type-specific barcodes and account for batch effect variation, and probabilistically annotates cell-type identity. The barcoding approach also provides a new way to discover marker genes. Using a range of datasets, including those generated to represent imperfect real-world reference data, we demonstrate that our approach substantially outperforms current reference-based methods, in particular when predicting across studies. Our approach also demonstrates that current approaches based on unsupervised clustering lead to false discoveries related to novel cell-types.

Motivation Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels. Results We introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes. Availability and implementation R package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary. Supplementary information Supplementary data are available at Bioinformatics online.

Unlike conventional bulk measurements, single-cell protein analysis permits quantification of protein expression in individual cells. This has shed light on the cell-to-cell variation in heterogeneous biological systems, such as solid tumors, brain tissues, and developing embryos. Herein, a microfluidic method is developed to profile protein expression in individual cells by performing single-cell intracellular protein immunoassay in picoliter paired droplets. The high sensitivity of single-cell protein analysis on a chip is achieved by the confined reaction volume of picoliter droplets, efficient kinetic characteristics of the immunoassay through active mixing, and minimum single-cell protein loss by integrated operations. The abundance of an intracellular prostate specific antigen at the single-cell level is measured, and then the platform is applied to identify cell types and investigate heterogeneity within cell populations. Overall, a paired chip for single-cell immunoassay establishes a foundation for parallel, sensitive, and integrated protein quantification at the single-cell level and will find wide applications in the field of single-cell proteomics.

Klinefelter syndrome (KS) is a leading contributor to male infertility and is characterised by complex and diverse clinical features; however, genetic changes in the KS transcriptome remain largely unknown. We therefore used transcriptomic and single-cell RNA sequencing (scRNA-seq) datasets from KS versus normal populations through the Gene Expression Omnibus (GEO) database to identify gene biomarkers associated with the occurrence of KS. We identified a total of 700 differentially expressed genes (DEGs) and completed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment pathway analysis and protein-protein interaction (PPI) network analysis. A total of four unreported KS-related hub genes (KIF2C, MRPS2, RPS15 and TSFM) were identified. Validation of the single-cell sequencing dataset showed that only KIF2C and RPS15 were expressed in spermatocytes and that they were differentially expressed in sperm cells. Further construction of the developmental trajectories of these two genes in sperm cells showed that the KIF2C gene showed an upward trend throughout the differentiation and development of sperm cells. In conclusion, we report here that KIF2C may be closely related to the differentiation and development of sperm cells in KS patients, which is important for revealing the molecular mechanism of KS and conducting further studies.