Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state (qG0), and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We demonstrate that the ccAFv2 classifier is generalizable across cell types and all three germ layers by applying it to developing fetal cells. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat or a PyPI package integrated with scanpy. We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

Cell identity revealed by precise cell cycle state mapping links data modalities

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predicting cell cycle phases based on scHi-C data remains a formidable challenge. Here, we present scHiCyclePred, a prediction model that integrates multiple feature sets to leverage scHi-C data for predicting cell cycle phases. scHiCyclePred extracts 3D chromatin structure features by incorporating multi-scale interaction information. The comparative analysis illustrates that scHiCyclePred surpasses existing methods such as Nagano_method and CIRCLET across various metrics including accuracy (ACC), F1 score, Precision, Recall, and balanced accuracy (BACC). In addition, we evaluate scHiCyclePred against the previously published CIRCLET using the dataset of complex tissues (Liu_dataset). Experimental results reveal significant improvements with scHiCyclePred exhibiting improvements of 0.39, 0.52, 0.52, and 0.39 over the CIRCLET in terms of ACC, F1 score, Precision, and Recall metrics, respectively. Furthermore, we conduct analyses on three-dimensional chromatin dynamics and gene features during the cell cycle, providing a more comprehensive understanding of cell cycle dynamics through chromatin structure. scHiCyclePred not only offers insights into cell biology but also holds promise for catalyzing breakthroughs in disease research. Access scHiCyclePred on GitHub at https:// github.com/HaoWuLab-Bioinformatics/ scHiCyclePred .

Destabilization of mRNAs enhances competence to initiate meiosis in mouse spermatogenic cells

The specialized cell cycle of meiosis transforms diploid germ cells into haploid gametes. In mammals, diploid spermatogenic cells acquire the competence to initiate meiosis in response to retinoic acid. Previous mouse studies revealed that MEIOC interacts with RNA-binding proteins YTHDC2 and RBM46 to repress mitotic genes and to promote robust meiotic gene expression in spermatogenic cells that have initiated meiosis. Here, we have used the enhanced resolution of scRNA-seq and bulk RNA-seq of developmentally synchronized spermatogenesis to define how MEIOC molecularly supports early meiosis in spermatogenic cells. We demonstrate that MEIOC mediates transcriptomic changes before meiotic initiation, earlier than previously appreciated. MEIOC, acting with YTHDC2 and RBM46, destabilizes its mRNA targets, including the transcriptional repressors E2f6 and Mga, in mitotic spermatogonia. MEIOC thereby derepresses E2F6- and MGA-repressed genes, including Meiosin and other meiosis-associated genes. This confers on spermatogenic cells the molecular competence to, in response to retinoic acid, fully activate the transcriptional regulator STRA8-MEIOSIN, which is required for the meiotic G1/S phase transition and for meiotic gene expression. We conclude that, in mice, mRNA decay mediated by MEIOC-YTHDC2-RBM46 enhances the competence of spermatogenic cells to initiate meiosis.

ScRNA-seq reveals dark- and light-induced differentially expressed gene atlases of seedling leaves in Arachis hypogaea L

Although the regulatory mechanisms of dark and light-induced plant morphogenesis have been broadly investigated, the biological process in peanuts has not been systematically explored on single-cell resolution. Herein, 10 cell clusters were characterized using scRNA-seq-identified marker genes, based on 13 409 and 11 296 single cells from 1-week-old peanut seedling leaves grown under dark and light conditions. 6104 genes and 50 transcription factors (TFs) displayed significant expression patterns in distinct cell clusters, which provided gene resources for profiling dark/light-induced candidate genes. Further pseudo-time trajectory and cell cycle evidence supported that dark repressed the cell division and perturbed normal cell cycle, especially the PORA abundances correlated with 11 TFs highly enriched in mesophyll to restrict the chlorophyllide synthesis. Additionally, light repressed the epidermis cell developmental trajectory extending by inhibiting the growth hormone pathway, and 21 TFs probably contributed to the different genes transcriptional dynamic. Eventually, peanut AHL17 was identified from the profile of differentially expressed TFs, which encoded protein located in the nucleus promoted leaf epidermal cell enlargement when ectopically overexpressed in Arabidopsis through the regulatory phytohormone pathway. Overall, our study presents the different gene atlases in peanut etiolated and green seedlings, providing novel biological insights to elucidate light-induced leaf cell development at the single-cell level.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

BACKGROUND

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models.

RESULTS

Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism.

CONCLUSIONS

Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

TDP2 is a regulator of estrogen-responsive oncogene expression

With its ligand estrogen, the estrogen receptor (ER) initiates a global transcriptional program, promoting cell growth. This process involves topoisomerase 2 (TOP2), a key protein in resolving topological issues during transcription by cleaving a DNA duplex, passing another duplex through the break, and repairing the break. Recent studies revealed the involvement of various DNA repair proteins in the repair of TOP2-induced breaks, suggesting potential alternative repair pathways in cases where TOP2 is halted after cleavage. However, the contribution of these proteins in ER-induced transcriptional regulation remains unclear. We investigated the role of tyrosyl-DNA phosphodiesterase 2 (TDP2), an enzyme for the removal of halted TOP2 from the DNA ends, in the estrogen-induced transcriptome using both targeted and global transcription analyses. MYC activation by estrogen, a TOP2-dependent and transient event, became prolonged in the absence of TDP2 in both TDP2-deficient cells and mice. Bulk and single-cell RNA-seq analyses defined MYC and CCND1 as oncogenes whose estrogen response is tightly regulated by TDP2. These results suggest that TDP2 may inherently participate in the repair of estrogen-induced breaks at specific genomic loci, exerting precise control over oncogenic gene expression.

Destabilization of mRNAs enhances competence to initiate meiosis in mouse spermatogenic cells

The specialized cell cycle of meiosis transforms diploid germ cells into haploid gametes. In mammals, diploid spermatogenic cells acquire the competence to initiate meiosis in response to retinoic acid. Previous mouse studies revealed that MEIOC interacts with RNA-binding proteins YTHDC2 and RBM46 to repress mitotic genes and promote robust meiotic gene expression in spermatogenic cells that have initiated meiosis. Here, we used the enhanced resolution of scRNA-seq, and bulk RNA-seq of developmentally synchronized spermatogenesis, to define how MEIOC molecularly supports early meiosis in spermatogenic cells. We demonstrate that MEIOC mediates transcriptomic changes before meiotic initiation, earlier than previously appreciated. MEIOC, acting with YTHDC2 and RBM46, destabilizes its mRNA targets, including transcriptional repressors E2f6 and Mga , in mitotic spermatogonia. MEIOC thereby derepresses E2F6- and MGA-repressed genes, including Meiosin and other meiosis-associated genes. This confers on spermatogenic cells the molecular competence to, in response to retinoic acid, fully activate transcriptional regulator STRA8-MEIOSIN, required for the meiotic G1/S phase transition and meiotic gene expression. We conclude that in mice, mRNA decay mediated by MEIOC-YTHDC2-RBM46 enhances the competence of spermatogenic cells to initiate meiosis.

SUMMARY STATEMENT

RNA-binding complex MEIOC-YTHDC2-RBM46 destabilizes its mRNA targets, including transcriptional repressors. This activity facilitates the retinoic acid-dependent activation of Meiosin gene expression and transition into meiosis.

Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells

Single-cell RNA sequencing (scRNAseq) is a rapidly advancing field enabling the characterisation of heterogeneous gene expression profiles within a population. The cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phases to cells within scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as only G1, S or G2/M. Existing discrete cell phase assignment tools are unable to differentiate between G2 and M and continuous-phase-assignment tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. In this study, bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence-activated cell sorting. These gene lists were used to develop a methodology which can distinguish G2 and M phase cells in scRNAseq datasets. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle to identify a mitotic-specific cell population.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

Isogenic hiPSC models of Turner syndrome development reveal shared roles of inactive X and Y in the human cranial neural crest network

Modeling the developmental etiology of viable human aneuploidy can be challenging in rodents due to syntenic boundaries, or primate-specific biology. In humans, monosomy-X (45,X) causes Turner syndrome (TS), altering craniofacial, skeletal, endocrine, and cardiovascular development, which in contrast remain unaffected in 39,X-mice. To learn how human monosomy-X may impact early embryonic development, we turned to human 45,X and isogenic euploid induced pluripotent stem cells (hiPSCs) from male and female mosaic donors. Because neural crest (NC) derived cell types are hypothesized to underpin craniofacial and cardiovascular changes in TS, we performed a highly-powered differential expression study on hiPSC-derived anterior neural crest cells (NCCs). Across three independent isogenic panels, 45,X NCCs show impaired acquisition of PAX7+SOX10+ markers, and disrupted expression of other NCC-specific genes, relative to their isogenic euploid controls. In particular, 45,X NCCs increase cholesterol biosynthesis genes while reducing transcripts that feature 5' terminal oligopyrimidine (TOP) motifs, including those of ribosomal protein and nuclear-encoded mitochondrial genes. Such metabolic pathways are also over-represented in weighted co-expression gene modules that are preserved in monogenic neurocristopathy. Importantly, these gene modules are also significantly enriched in 28% of all TS-associated terms of the human phenotype ontology. Our analysis identifies specific sex-linked genes that are expressed from two copies in euploid males and females alike and qualify as candidate haploinsufficient drivers of TS phenotypes in NC-derived lineages. This study demonstrates that isogenic hiPSC-derived NCC panels representing monosomy-X can serve as a powerful model of early NC development in TS and inform new hypotheses towards its etiology.

Single-cell profiling of MC1R-inhibited melanocytes

The human red hair color (RHC) trait is caused by increased pheomelanin (red-yellow) and reduced eumelanin (black-brown) pigment in skin and hair due to diminished melanocortin 1 receptor (MC1R) function. In addition, individuals harboring the RHC trait are predisposed to melanoma development. While MC1R variants have been established as causative of RHC and are a well-defined risk factor for melanoma, it remains unclear mechanistically why decreased MC1R signaling alters pigmentation and increases melanoma susceptibility. Here, we use single-cell RNA sequencing (scRNA-seq) of melanocytes isolated from RHC mouse models to define a MC1R-inhibited Gene Signature (MiGS) comprising a large set of previously unidentified genes which may be implicated in melanogenesis and oncogenic transformation. We show that one of the candidate MiGS genes, TBX3, a well-known anti-senescence transcription factor implicated in melanoma progression, binds both E-box and T-box elements to regulate genes associated with melanogenesis and senescence bypass. Our results provide key insights into further mechanisms by which melanocytes with reduced MC1R signaling may regulate pigmentation and offer new candidates of study toward understanding how individuals with the RHC phenotype are predisposed to melanoma.

From G1 to M: a comparative study of methods for identifying cell cycle phases

Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

Monitoring the Cell Cycle of Tumor Cells in Mouse Models of Human Cancer

Cell division is obligatory to tumor growth. However, both cancer cells and noncancer cells in tumors can be found in distinct stages of the cell cycle, which may inform the growth potential of these tumors, their propensity to metastasize, and their response to therapy. Hence, it is of utmost importance to monitor the cell cycle of tumor cells. Here we discuss well-established methods and new genetic advances to track the cell cycle of tumor cells in mouse models of human cancer. We also review recent genetic studies investigating the role of the cell-cycle machinery in the growth of tumors in vivo, with a focus on the machinery regulating the G1/S transition of the cell cycle.

Multi-Omic Analysis of CIC's Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity

CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC's interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3' and 5' untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC's functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC's potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts.

Spatiotemporal Characterization of Human Early Intervertebral Disc Formation at Single-Cell Resolution

The intervertebral disc (IVD) acts as a fibrocartilaginous joint to anchor adjacent vertebrae. Although several studies have demonstrated the cellular heterogeneity of adult mature IVDs, a single-cell transcriptomic atlas mapping early IVD formation is still lacking. Here, the authors generate a spatiotemporal and single cell-based transcriptomic atlas of human IVD formation at the embryonic stage and a comparative mouse transcript landscape. They identify two novel human notochord (NC)/nucleus pulposus (NP) clusters, SRY-box transcription factor 10 (SOX10)⁺ and cathepsin K (CTSK)⁺ , that are distributed in the early and late stages of IVD formation and they are validated by lineage tracing experiments in mice. Matrisome NC/NP clusters, T-box transcription factor T (TBXT)⁺ and CTSK⁺ , are responsible for the extracellular matrix homeostasis. The IVD atlas suggests that a subcluster of the vertebral chondrocyte subcluster might give rise to an inner annulus fibrosus of chondrogenic origin, while the fibroblastic outer annulus fibrosus preferentially expresseds transgelin and fibromodulin . Through analyzing intercellular crosstalk, the authors further find that notochordal secreted phosphoprotein 1 (SPP1) is a novel cue in the IVD microenvironment, and it is associated with IVD development and degeneration. In conclusion, the single-cell transcriptomic atlas will be leveraged to develop preventative and regenerative strategies for IVD degeneration.

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

Loss of MC1R signaling implicates TBX3 in pheomelanogenesis and melanoma predisposition

The human Red Hair Color (RHC) trait is caused by increased pheomelanin (red-yellow) and reduced eumelanin (black-brown) pigment in skin and hair due to diminished melanocortin 1 receptor (MC1R) function. In addition, individuals harboring the RHC trait are predisposed to melanoma development. While MC1R variants have been established as causative of RHC and are a well-defined risk factor for melanoma, it remains unclear mechanistically why decreased MC1R signaling alters pigmentation and increases melanoma susceptibility. Here, we use single-cell RNA-sequencing (scRNA-seq) of melanocytes isolated from RHC mouse models to reveal a Pheomelanin Gene Signature (PGS) comprising genes implicated in melanogenesis and oncogenic transformation. We show that TBX3, a well-known anti-senescence transcription factor implicated in melanoma progression, is part of the PGS and binds both E-box and T-box elements to regulate genes associated with melanogenesis and senescence bypass. Our results provide key insights into mechanisms by which MC1R signaling regulates pigmentation and how individuals with the RHC phenotype are predisposed to melanoma.

Complex Analysis of Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.

Aortic Stress Activates an Adaptive Program in Thoracic Aortic Smooth Muscle Cells That Maintains Aortic Strength and Protects Against Aneurysm and Dissection in Mice

BACKGROUND

When aortic cells are under stress, such as increased hemodynamic pressure, they adapt to the environment by modifying their functions, allowing the aorta to maintain its strength. To understand the regulation of this adaptive response, we examined transcriptomic and epigenomic programs in aortic smooth muscle cells (SMCs) during the adaptive response to AngII (angiotensin II) infusion and determined its importance in protecting against aortic aneurysm and dissection (AAD).

METHODS

We performed single-cell RNA sequencing and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) analyses in a mouse model of sporadic AAD induced by AngII infusion. We also examined the direct effects of YAP (yes-associated protein) on the SMC adaptive response in vitro. The role of YAP in AAD development was further evaluated in AngII-infused mice with SMC-specific Yap deletion.

RESULTS

In wild-type mice, AngII infusion increased medial thickness in the thoracic aorta. Single-cell RNA sequencing analysis revealed an adaptive response in thoracic SMCs characterized by upregulated genes with roles in wound healing, elastin and collagen production, proliferation, migration, cytoskeleton organization, cell-matrix focal adhesion, and PI3K-PKB/Akt (phosphoinositide-3-kinase-protein kinase B/Akt) and TGF-β (transforming growth factor beta) signaling. ScATAC-seq analysis showed increased chromatin accessibility at regulatory regions of adaptive genes and revealed the mechanical sensor YAP/transcriptional enhanced associate domains as a top candidate transcription complex driving the expression of these genes (eg, Lox, Col5a2, Tgfb2). In cultured human aortic SMCs, cyclic stretch activated YAP, which directly bound to adaptive gene regulatory regions (eg, Lox) and increased their transcript abundance. SMC-specific Yap deletion in mice compromised this adaptive response in SMCs, leading to an increased AAD incidence.

CONCLUSIONS

Aortic stress triggers the systemic epigenetic induction of an adaptive response (eg, wound healing, proliferation, matrix organization) in thoracic aortic SMCs that depends on functional biomechanical signal transduction (eg, YAP signaling). Our study highlights the importance of the adaptive response in maintaining aortic homeostasis and preventing AAD in mice.

Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRASG12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRASG12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

Populations of stem, progenitor, or cancer cells show proliferative heterogeneity in vivo, comprising proliferating and quiescent cells. Consistent quantification of the quiescent subpopulation and progression of the proliferating cells through the individual phases of the cell cycle has not been achieved. Here, we describe CycleFlow, a method that robustly infers this comprehensive information from standard pulse-chase experiments with thymidine analogs. Inference is based on a mathematical model of the cell cycle, with realistic waiting time distributions for the G1, S, and G2/M phases and a long-term quiescent G0 state. We validate CycleFlow with an exponentially growing cancer cell line in vitro. Applying it to T cell progenitors in steady state in vivo, we uncover strong proliferative heterogeneity, with a minority of CD4+CD8+ T cell progenitors cycling very rapidly and then entering quiescence. CycleFlow is suitable as a routine method for quantitative cell-cycle analysis.

Endothelial cell cycle state determines propensity for arterial-venous fate

During blood vessel development, endothelial cells become specified toward arterial or venous fates to generate a circulatory network that provides nutrients and oxygen to, and removes metabolic waste from, all tissues. Arterial-venous specification occurs in conjunction with suppression of endothelial cell cycle progression; however, the mechanistic role of cell cycle state is unknown. Herein, using Cdh5-CreERT2;R26FUCCI2aR reporter mice, we find that venous endothelial cells are enriched for the FUCCI-Negative state (early G1) and BMP signaling, while arterial endothelial cells are enriched for the FUCCI-Red state (late G1) and TGF-β signaling. Furthermore, early G1 state is essential for BMP4-induced venous gene expression, whereas late G1 state is essential for TGF-β1-induced arterial gene expression. Pharmacologically induced cell cycle arrest prevents arterial-venous specification defects in mice with endothelial hyperproliferation. Collectively, our results show that distinct endothelial cell cycle states provide distinct windows of opportunity for the molecular induction of arterial vs. venous fate.

Monosomy X in isogenic human iPSC-derived trophoblast model impacts expression modules preserved in human placenta

Mammalian sex chromosomes encode homologous X/Y gene pairs that were retained on the Y chromosome in males and escape X chromosome inactivation (XCI) in females. Inferred to reflect X/Y pair dosage sensitivity, monosomy X is a leading cause of miscarriage in humans with near full penetrance. This phenotype is shared with many other mammals but not the mouse, which offers sophisticated genetic tools to generate sex chromosomal aneuploidy but also tolerates its developmental impact. To address this critical gap, we generated X-monosomic human induced pluripotent stem cells (hiPSCs) alongside otherwise isogenic euploid controls from male and female mosaic samples. Phased genomic variants in these hiPSC panels enable systematic investigation of X/Y dosage-sensitive features using in vitro models of human development. Here, we demonstrate the utility of these validated hiPSC lines to test how X/Y-linked gene dosage impacts a widely used model for human syncytiotrophoblast development. While these isogenic panels trigger a GATA2/3- and TFAP2A/C-driven trophoblast gene circuit irrespective of karyotype, differential expression implicates monosomy X in altered levels of placental genes and in secretion of placental growth factor (PlGF) and human chorionic gonadotropin (hCG). Remarkably, weighted gene coexpression network modules that significantly reflect these changes are also preserved in first-trimester chorionic villi and term placenta. Our results suggest monosomy X may skew trophoblast cell type composition and function, and that the combined haploinsufficiency of the pseudoautosomal region likely plays a key role in these changes.

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Background

Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros).

Methods

To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells.

Results

Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms.

Conclusions

Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04778-9

Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes

BACKGROUND

Three-quarters of bladder cancer patients present with early-stage disease (non-muscle-invasive bladder cancer, NMIBC, UICC TNM stages Ta, T1 and Tis); however, most next-generation sequencing studies to date have concentrated on later-stage disease (muscle-invasive BC, stages T2+). We used exome and transcriptome sequencing to comprehensively characterise NMIBCs of all grades and stages to identify prognostic genes and pathways that could facilitate treatment decisions. Tumour grading is based upon microscopy and cellular appearances (grade 1 BCs are less aggressive, and grade 3 BCs are most aggressive), and we chose to also focus on the most clinically complex NMIBC subgroup, those patients with grade 3 pathological stage T1 (G3 pT1) disease.

METHODS

Whole-exome and RNA sequencing were performed in total on 96 primary NMIBCs including 22 G1 pTa, 14 G3 pTa and 53 G3 pT1s, with both exome and RNA sequencing data generated from 75 of these individual samples. Associations between genomic alterations, expression profiles and progression-free survival (PFS) were investigated.

RESULTS

NMIBCs clustered into 3 expression subtypes with different somatic alteration characteristics. Amplifications of ARNT and ERBB2 were significant indicators of worse PFS across all NMIBCs. High APOBEC mutagenesis and high tumour mutation burden were both potential indicators of better PFS in G3pT1 NMIBCs. The expression of individual genes was not prognostic in BCG-treated G3pT1 NMIBCs; however, downregulated interferon-alpha and gamma response pathways were significantly associated with worse PFS (adjusted p-value < 0.005).

CONCLUSIONS

Multi-omic data may facilitate better prognostication and selection of therapeutic interventions in patients with G3pT1 NMIBC. These findings demonstrate the potential for improving the management of high-risk NMIBC patients and warrant further prospective validation.

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts.

Single-cell RNA-sequencing technology gives access to cell cycle dynamics without externally perturbing the cell. Here the authors present DeepCycle,a robust deep learning method to infer the cell cycle state in single cells from scRNA-seq data.

Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches

Cell cycle is a biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We also develop a simplified kinetic model with piecewise-constant kinetic rates describing the dynamics of lumps of genes involved in S-phase and G2/M phases. We show how the developed cell cycle models can be applied to analyze the available single cell datasets and simulate certain properties of the observed cell cycle trajectories. Based on our model, we can predict with good accuracy the cell line doubling time from the length of cell cycle trajectory.

Universal prediction of cell-cycle position using transfer learning

BACKGROUND

The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.

RESULTS

Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.

CONCLUSIONS

Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

CCPE: cell cycle pseudotime estimation for single cell RNA-seq data

Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and identify cell cycle phases from scRNA-seq data. CCPE uses a discriminative helix to characterize the circular process of the cell cycle and estimates each cell's pseudotime along the cell cycle. We evaluated the performance of CCPE based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various applications compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. Accurate prediction of the cell cycle using CCPE can also effectively facilitate the removal of cell cycle effects across cell types or conditions.

Time-resolved single-cell sequencing identifies multiple waves of mRNA decay during the mitosis-to-G1 phase transition

Accurate control of the cell cycle is critical for development and tissue homeostasis, and requires precisely timed expression of many genes. Cell cycle gene expression is regulated through transcriptional and translational control, as well as through regulated protein degradation. Here, we show that widespread and temporally controlled mRNA decay acts as an additional mechanism for gene expression regulation during the cell cycle in human cells. We find that two waves of mRNA decay occur sequentially during the mitosis-to-G1 phase transition, and we identify the deadenylase CNOT1 as a factor that contributes to mRNA decay during this cell cycle transition. Collectively, our data show that, akin to protein degradation, scheduled mRNA decay helps to reshape cell cycle gene expression as cells move from mitosis into G1 phase.

Single-cell reconstruction with spatial context of migrating neural crest cells and their microenvironments during vertebrate head and neck formation

The dynamics of multipotent neural crest cell differentiation and invasion as cells travel throughout the vertebrate embryo remain unclear. Here, we preserve spatial information to derive the transcriptional states of migrating neural crest cells and the cellular landscape of the first four chick cranial to cardiac branchial arches (BA1-4) using label-free, unsorted single-cell RNA sequencing. The faithful capture of branchial arch-specific genes led to identification of novel markers of migrating neural crest cells and 266 invasion genes common to all BA1-4 streams. Perturbation analysis of a small subset of invasion genes and time-lapse imaging identified their functional role to regulate neural crest cell behaviors. Comparison of the neural crest invasion signature to other cell invasion phenomena revealed a shared set of 45 genes, a subset of which showed direct relevance to human neuroblastoma cell lines analyzed after exposure to the in vivo chick embryonic neural crest microenvironment. Our data define an important spatio-temporal reference resource to address patterning of the vertebrate head and neck, and previously unidentified cell invasion genes with the potential for broad impact.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity-often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.

Human melanocyte development and melanoma dedifferentiation at single-cell resolution

In humans, epidermal melanocytes are responsible for skin pigmentation, defence against ultraviolet radiation and the deadliest common skin cancer, melanoma. Although there is substantial overlap in melanocyte development pathways between different model organisms, species-dependent differences are frequent and the conservation of these processes in human skin remains unresolved. Here, we used a single-cell enrichment and RNA-sequencing pipeline to study human epidermal melanocytes directly from the skin, capturing transcriptomes across different anatomical sites, developmental age, sexes and multiple skin tones. We uncovered subpopulations of melanocytes that exhibit anatomical site-specific enrichment that occurs during gestation and persists through adulthood. The transcriptional signature of the volar-enriched subpopulation is retained in acral melanomas. Furthermore, we identified human melanocyte differentiation transcriptional programs that are distinct from gene signatures generated from model systems. Finally, we used these programs to define patterns of dedifferentiation that are predictive of melanoma prognosis and response to immune checkpoint inhibitor therapy.

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

Mathematical Modelling Based on In Vivo Imaging Suggests CD137-Stimulated Cytotoxic T Lymphocytes Exert Superior Tumour Control Due to an Enhanced Antimitotic Effect on Tumour Cells

Simple Summary

Cytotoxic T lymphocytes (CTLs) play an important role in controlling tumours, and an improved understanding of how they accomplish this will benefit immunotherapeutic cancer treatment strategies. Stimulation of CTLs by targeting their CD137 receptor is a strategy currently under investigation for enhancing responses against tumours, yet so far only limited quantitative knowledge regarding the effects of such stimulation upon CTLs has been obtained. Here, we develop mathematical models to describe dynamic in vivo two-photon imaging of tumour infiltrating CTLs, to characterise differences in their function either in the presence or absence of a CD137 agonist antibody. We showed that an increased antiproliferative effect and a more sustained presence of CTLs within the tumour were the most important effects associated with anti-CD137 treatment.

Abstract

Several immunotherapeutic strategies for the treatment of cancer are under development. Two prominent strategies are adoptive cell transfer (ACT) of CTLs and modulation of CTL function with immune checkpoint inhibitors or with costimulatory antibodies. Despite some success with these approaches, there remains a lack of detailed and quantitative descriptions of the events following CTL transfer and the impact of immunomodulation. Here, we have applied ordinary differential equation models to two photon imaging data derived from a B16F10 murine melanoma. Models were parameterised with data from two different treatment conditions: either ACT-only, or ACT with intratumoural costimulation using a CD137 targeted antibody. Model dynamics and best fitting parameters were compared, in order to assess the mode of action of the CTLs and examine how the CD137 antibody influenced their activities. We found that the cytolytic activity of the transferred CTLs was minimal without CD137 costimulation, and that the CD137 targeted antibody did not enhance the per-capita killing ability of the transferred CTLs. Instead, the results of our modelling study suggest that an antiproliferative effect of CTLs exerted upon the tumour likely accounted for the majority of the reduction in tumour growth after CTL transfer. Moreover, we found that CD137 most likely improved tumour control via enhancement of this antiproliferative effect, as well as prolonging the period in which CTLs were inside the tumour, leading to a sustained duration of their antitumour effects following CD137 stimulation.

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Spatiotemporal dissection of the cell cycle with single-cell proteogenomics

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer^1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Automated methods for cell type annotation on scRNA-seq data

The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.

Transcriptional profiling of pediatric cholestatic livers identifies three distinct macrophage populations

Background & aims

Limited understanding of the role for specific macrophage subsets in the pathogenesis of cholestatic liver injury is a barrier to advancing medical therapy. Macrophages have previously been implicated in both the mal-adaptive and protective responses in obstructive cholestasis. Recently two macrophage subsets were identified in non-diseased human liver; however, no studies to date fully define the heterogeneous macrophage subsets during the pathogenesis of cholestasis. Here, we aim to further characterize the transcriptional profile of macrophages in pediatric cholestatic liver disease.

Methods

We isolated live hepatic immune cells from patients with biliary atresia (BA), Alagille syndrome (ALGS), and non-cholestatic pediatric liver by fluorescence activated cell sorting. Through single-cell RNA sequencing analysis and immunofluorescence, we characterized cholestatic macrophages. We next compared the transcriptional profile of pediatric cholestatic and non-cholestatic macrophage populations to previously published data on normal adult hepatic macrophages.

Results

We identified 3 distinct macrophage populations across cholestatic liver samples and annotated them as lipid-associated macrophages, monocyte-like macrophages, and adaptive macrophages based on their transcriptional profile. Immunofluorescence of liver tissue using markers for each subset confirmed their presence across BA (n = 6) and ALGS (n = 6) patients. Cholestatic macrophages demonstrated reduced expression of immune regulatory genes as compared to normal hepatic macrophages and were distinct from macrophage populations defined in either healthy adult or pediatric non-cholestatic liver.

Conclusions

We are the first to perform single-cell RNA sequencing on human pediatric cholestatic liver and identified three macrophage subsets with distinct transcriptional signatures from healthy liver macrophages. Further analyses will identify similarities and differences in these macrophage sub-populations across etiologies of cholestatic liver disease. Taken together, these findings may allow for future development of targeted therapeutic strategies to reprogram macrophages to an immune regulatory phenotype and reduce cholestatic liver injury.

Single-Cell Profiling Reveals Divergent, Globally Patterned Immune Responses in Murine Skin Inflammation

Inflammatory response heterogeneity has impeded high-resolution dissection of diverse immune cell populations during activation. We characterize mouse cutaneous immune cells by single-cell RNA sequencing, after inducing inflammation using imiquimod and oxazolone dermatitis models. We identify 13 CD45⁺ subpopulations, which broadly represent most functionally characterized immune cell types. Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and antigen-presenting cells (APCs). Oxazolone also induces Il4/Il13 expression in newly infiltrating basophils, and Il4ra and Ccl24, most prominently in APCs. In contrast, imiquimod broadly upregulates Il17/Il22 and Ccl4/Ccl5. A comparative analysis of single-cell inflammatory transcriptional responses reveals that APC response to oxazolone is tightly restricted by cell identity, whereas imiquimod enforces shared programs on multiple APC populations in parallel. These global molecular patterns not only contrast immune responses on a systems level but also suggest that the mechanisms of new sources of inflammation can eventually be deduced by comparison to known signatures.

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Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and APCs

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Il4/Il13 induction in skin by oxazolone is dominated by infiltrating basophils

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Imiquimod broadly increases Il17/Il22 and Ccl4/Ccl5, extending to non-T cells

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Oxazolone induces more highly compartmentalized immune cell responses than imiquimod