scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors

Role of epigenetics in paediatric cancer pathogenesis & drug resistance

Paediatric oncogenesis is tightly intertwined with errors in developmental processes involving cell specification and differentiation, which are governed by intricate temporal epigenetic signals. As paediatric cancers are characterised by a low number of somatic mutations, dysregulated chromatin landscapes are believed to be key drivers of oncogenesis. Epigenetic dysregulation is induced by mutations and aberrant expression of histones and epigenetic regulatory genes, to altered DNA methylation patterns and dysregulated noncoding RNA expression. In this review, we discuss epigenetic alterations in paediatric cancer oncogenesis and recurrence, and their potential as diagnostic biomarkers. We also discuss various epigenetic drugs that have entered clinical trials for aggressive paediatric cancers. Targeting paediatric-specific epigenetic vulnerabilities may improve recurrence-free survival in high-risk cancers.

Characterizing resistant cellular states in nasopharyngeal carcinoma during EBV lytic induction

The pervasive occurrence of nasopharyngeal carcinoma (NPC) is intricately linked to Epstein-Barr virus (EBV) infection, making EBV and its associated pathways promising therapeutic targets for NPC and other EBV-related cancers. Lytic induction therapy, an emerging virus-targeted therapeutic strategy, capitalizes on the presence of EBV in tumor cells to specifically induce cytotoxicity against EBV-associated malignancies. Despite the expanding repertoire of compounds developed to induce EBV lytic reactivation, achieving universal induction across all infected cells remains elusive. The inherent heterogeneity of tumor cells likely contributes to this variability. In this study, we used the NPC43 cell line, an EBV-positive NPC in vitro model, and single-cell transcriptomics to characterize the diverse cellular responses to EBV lytic induction. Our longitudinal monitoring revealed a distinctive lytic induction non-responsive cellular state characterized by elevated expression of SOX2 and NTRK2. Cells in this state exhibit phenotypic similarities to cancer stem cells (CSCs), and we verified the roles of SOX2 and NTRK2 in manifesting these phenotypes. Our findings reveal a significant challenge for lytic induction therapy, as not all tumor cells are equally susceptible. These insights highlight the importance of combining lytic induction with therapies targeting CSC-like properties to enhance treatment efficacy for NPC and other EBV-associated cancers.

Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.

Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets

Copy number alterations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor versus normal cell classification; CNAs profile accuracy, tumor subclone inference, and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.

Scalable co-sequencing of RNA and DNA from individual nuclei

The ideal technology for directly investigating the relationship between genotype and phenotype would analyze both RNA and DNA genome-wide and with single-cell resolution; however, existing tools lack the throughput required for comprehensive analysis of complex tumors and tissues. We introduce a highly scalable method for jointly profiling DNA and expression following nucleosome depletion (DEFND-seq). In DEFND-seq, nuclei are nucleosome-depleted, tagmented and separated into individual droplets for messenger RNA and genomic DNA barcoding. Once nuclei have been depleted of nucleosomes, subsequent steps can be performed using the widely available 10x Genomics droplet microfluidic technology and commercial kits. We demonstrate the production of high-complexity mRNA and gDNA sequencing libraries from thousands of individual nuclei from cell lines, fresh and archived surgical specimens for associating gene expression with both copy number and single-nucleotide variants.

Exploring genome-transcriptome correlations in cancer

We examine the complex relationship between genomic copy number variation (CNV) and gene expression, highlighting the relevance to cancer biology and other biological contexts. By tracing the history of genometranscriptome correlations, we emphasize the complexity and challenges in understanding these interactions, particularly within the heterogeneous landscape of human cancers. Recent advances in computational algorithms and high-throughput single-cell multi-omic sequencing technologies are discussed, demonstrating their potential to refine our understanding of cancer biology and their limitations. The integration of genomic and transcriptomic analyses, which offers novel insights into tumor evolution and heterogeneity as well as therapeutic strategies, is presented as a crucial approach for advancing cancer research.

Intratumor microbiome-derived butyrate promotes chemo-resistance in colorectal cancer

Introduction

Colorectal cancer (CRC) is a leading cause of cancer-related mortality globally. Although tumor immunotherapy is widely recognized for treating unresectable CRC, challenges such as ineffective immunotherapy and drug resistance remain prevalent. While intratumor microbiome-derived butyrate has been implicated in promoting lung cancer metastasis, its role in CRC chemoresistance is not well understood. This study aimed to explore the relationship between intratumor butyrate and chemoresistance in CRC.

Methods

We performed a comprehensive analysis of the microbiome composition in CRC patients with varying resistance-free survival (RFS) durations, utilizing 16S rRNA sequencing. Furthermore, we assessed the prognostic significance of circulating microbiome DNA (cmDNA) and examined the effects of exogenous butyrate supplementation on the chemosensitivity of CRC cell lines.

Results

Our 16S sequencing analysis revealed a reduction in microbial diversity within tumor samples of patients with resistance, as indicated by metrics such as observed taxonomic units, Shannon, and Simpson indices. Notably, Roseburia and Fusobacteria emerged as prominent biomarkers for the resistance group, whereas Bifidobacterium, Helicobacter, and Akkermansia were identified as biomarkers for the non-resistant group. Utilizing a Lasso regression model, we identified six genera-Roseburia, Helicobacter, Gardnerella, Flavonifractor, Coprococcus, and Anaerostipes-that significantly correlated with recurrence-free survival. Furthermore, both the intratumor microbiome signature and circulating microbiome DNA were effective in accurately predicting CRC resistance. Experimental assays, including CCK8 and wound-healing, demonstrated that intratumor microbiome-derived butyrate enhances the proliferation and migration of HCT15 cells in a time- and concentration-dependent manner. Cell survival analysis further indicated that butyrate treatment significantly increased the IC50 value, suggesting heightened drug resistance in HCT15 cells. Mechanistically, this resistance was attributed to butyrate's activation of the PI3K-AKT signaling pathway.

Conclusion

Our results suggest that intratumor microbiome-derived butyrate contributes to chemoresistance in colorectal cancer, highlighting the potential prognostic and therapeutic significance of the intratumor microbiome.

Targeted mutagenesis of specific genomic DNA sequences in animals for the in vivo generation of variant libraries

Understanding how the number, placement and affinity of transcription factor binding sites dictates gene regulatory programs remains a major unsolved challenge in biology, particularly in the context of multicellular organisms. To uncover these rules, it is first necessary to find the binding sites within a regulatory region with high precision, and then to systematically modulate this binding site arrangement while simultaneously measuring the effect of this modulation on output gene expression. Massively parallel reporter assays (MPRAs), where the gene expression stemming from 10,000s of in vitro-generated regulatory sequences is measured, have made this feat possible in high-throughput in single cells in culture. However, because of lack of technologies to incorporate DNA libraries, MPRAs are limited in whole organisms. To enable MPRAs in multicellular organisms, we generated tools to create a high degree of mutagenesis in specific genomic loci in vivo using base editing. Targeting GFP integrated in the genome of Drosophila cell culture and whole animals as a case study, we show that the base editor AIDevoCDA1 stemming from sea lamprey fused to nCas9 is highly mutagenic. Surprisingly, longer gRNAs increase mutation efficiency and expand the mutating window, which can allow the introduction of mutations in previously untargetable sequences. Finally, we demonstrate arrays of >20 gRNAs that can efficiently introduce mutations along a 200bp sequence, making it a promising tool to test enhancer function in vivo in a high throughput manner.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

DMF-Bimol: Counting mRNA and Protein Molecules in Single Cells with Digital Microfluidics

Analyzing single-cell protein and mRNA levels yields invaluable insights into cellular functions and the intricacies of biologically heterogeneous systems. Current joint mRNAs and protein analysis methodologies suffer from relative quantification, low sensitivity, possible background interference, and tedious manual manipulation. Therefore, we propose DMF-Bimol that leverages addressable digital microfluidics to automate digital counting of single-cell mRNA and protein based on proximity ligation assay (PLA) and one-step RT-droplet digital PCR (RT-ddPCR). Through an engineered hydrophilic-hydrophobic interface, DMF-Bimol enables efficient single-cell isolation and lossless protein and nucleic acid processing. The closed droplet reaction system enhances the protein concentration and isolates exogenous contaminants, thereby dramatically improving the efficiency of the PLA reaction. The limit of detection of this approach achieves 3313 protein copies, marking a significant 17-fold enhancement in sensitivity over traditional benchtop PLA. This heightened sensitivity also uncovers a lower correlation between mRNA and protein levels in individual cells (Spearman r = 0.255) than bulk results, reflecting the complex relationship in heterogeneous cells. Using DMF-Bimol, we observed a significant upsurge of CD147 protein in CD138+ myeloma cells but consistent levels of CD147 mRNAs compared with normal leukocytes. This discovery indicates a possible consequence of CD147 oncogenic activation that tends to harness protein translation to bolster tumor cell survival and enhance invasiveness, highlighting the potential of DMF-Bimol in unveiling intricate dynamics in translation processes at the single-cell level.

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BACKGROUND

Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.

RESULTS

We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.

CONCLUSIONS

Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution

BACKGROUND

Tumors are able to acquire new capabilities, including traits such as drug resistance and metastasis that are associated with unfavorable clinical outcomes. Single-cell technologies have made it possible to study both mutational and transcriptomic profiles, but as most studies have been conducted on model systems, little is known about cancer evolution in human patients. Hence, a better understanding of cancer evolution could have important implications for treatment strategies.

RESULTS

Here, we analyze cancer evolution and clonal selection by jointly considering mutational and transcriptomic profiles of single cells acquired from tumor biopsies from 49 lung cancer samples and 51 samples with chronic myeloid leukemia. Comparing the two profiles, we find that each clone is associated with a preferred transcriptional state. For metastasis and drug resistance, we find that the number of mutations affecting related genes increases as the clone evolves, while changes in gene expression profiles are limited. Surprisingly, we find that mutations affecting ligand-receptor interactions with the tumor microenvironment frequently emerge as clones acquire drug resistance.

CONCLUSIONS

Our results show that lung cancer and chronic myeloid leukemia maintain a high clonal and transcriptional diversity, and we find little evidence in favor of clonal sweeps. This suggests that for these cancers selection based solely on growth rate is unlikely to be the dominating driving force during cancer evolution.

Principled and interpretable alignability testing and integration of single-cell data

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features. SMAI provides a statistical test to robustly assess the alignability between datasets to avoid misleading inference and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

Integrative genotyping of cancer and immune phenotypes by long-read sequencing

Single-cell transcriptomics has become the definitive method for classifying cell types and states, and can be augmented with genotype information to improve cell lineage identification. Due to constraints of short-read sequencing, current methods to detect natural genetic barcodes often require cumbersome primer panels and early commitment to targets. Here we devise a flexible long-read sequencing workflow and analysis pipeline, termed nanoranger, that starts from intermediate single-cell cDNA libraries to detect cell lineage-defining features, including single-nucleotide variants, fusion genes, isoforms, sequences of chimeric antigen and TCRs. Through systematic analysis of these classes of natural 'barcodes', we define the optimal targets for nanoranger, namely those loci close to the 5' end of highly expressed genes with transcript lengths shorter than 4 kB. As proof-of-concept, we apply nanoranger to longitudinal tracking of subclones of acute myeloid leukemia (AML) and describe the heterogeneous isoform landscape of thousands of marrow-infiltrating immune cells. We propose that enhanced cellular genotyping using nanoranger can improve the tracking of single-cell tumor and immune cell co-evolution.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Parentage influence on gene expression under acidification revealed through single-embryo sequencing

The dissolution of anthropogenic carbon dioxide (CO2 ) in seawater has altered its carbonate chemistry in the process of ocean acidification (OA). OA affects the viability of marine species. In particular, calcifying organisms and their early planktonic larval stages are considered vulnerable. These organisms often utilize energy reserves for metabolism rather than growth and calcification as supported by bulk RNA-sequencing (RNA-seq) experiments. Yet, transcriptomic profiling of a bulk sample reflects the average gene expression of the population, neglecting the variations between individuals, which forms the basis for natural selection. Here, we used single-embryo RNA-seq on larval sea urchin Heliocidaris crassispina, which is a commercially and ecologically valuable species in East Asia, to document gene expression changes to OA at an individual and family level. Three paternal half-sibs groups were fertilized and exposed to 3 pH conditions (ambient pH 8.0, 7.7 and 7.4) for 12 h prior to sequencing and oxygen consumption assay. The resulting transcriptomic profile of all embryos can be distinguished into four clusters, with differences in gene expressions that govern biomineralization, cell differentiation and patterning, as well as metabolism. While these responses were influenced by pH conditions, the male identities also had an effect. Specifically, a regression model and goodness of fit tests indicated a significant interaction between sire and pH on the probability of embryo membership in different clusters of gene expression. The single-embryo RNA-seq approach is promising in climate stressor research because not only does it highlight potential impacts before phenotypic changes were observed, but it also highlights variations between individuals and lineages, thus enabling a better determination of evolutionary potential.

Pooled Genome-Scale CRISPR Screens in Single Cells

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.

Chromosome instability and aneuploidy in the mammalian brain

This review investigates the role of aneuploidy and chromosome instability (CIN) in the aging brain. Aneuploidy refers to an abnormal chromosomal count, deviating from the normal diploid set. It can manifest as either a deficiency or excess of chromosomes. CIN encompasses a broader range of chromosomal alterations, including aneuploidy as well as structural modifications in DNA. We provide an overview of the state-of-the-art methodologies utilized for studying aneuploidy and CIN in non-tumor somatic tissues devoid of clonally expanded populations of aneuploid cells.CIN and aneuploidy, well-established hallmarks of cancer cells, are also associated with the aging process. In non-transformed cells, aneuploidy can contribute to functional impairment and developmental disorders. Despite the importance of understanding the prevalence and specific consequences of aneuploidy and CIN in the aging brain, these aspects remain incompletely understood, emphasizing the need for further scientific investigations.This comprehensive review consolidates the present understanding, addresses discrepancies in the literature, and provides valuable insights for future research efforts.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

Profiling Single-Cell Genome and Transcriptome by scONE-Seq

Single-cell multi-omics technologies can provide a unique perspective on tumor cellular heterogeneity. We have developed a versatile method for simultaneous transcriptome and genome profiling of single cells or single nuclei in one tube reaction, named scONE-seq. It is conveniently compatible with frozen tissue from biobanks, which are a major source of patient samples for research. Here, we describe the detailed procedures to profile single-cell/nucleus transcriptome and genome. The sequencing library is compatible with both Illumina and MGI sequencers; it is also compatible with frozen tissue from biobanks, which are a major source of patient samples for research and drug discovery.