Single-cell Techniques Research Articles

Single-Cell Single-Molecule RNA-FISH Combined with Immunofluorescence and High-Speed and High-Resolution Scanning Analysis to Visualize the Reactivation of Latent HIV-1.

Latent HIV-1 reservoirs are a major obstacle to the eradication of HIV-1. Several cure strategies have been proposed to eliminate latent reservoirs. One of the key strategies involves the reactivation of latent HIV-1 from cells using latency-reversing agents. However, currently it is unclear whether any of the latency-reversing agents are able to completely reactivate HIV-1 provirus transcription in all latent cells. An understanding of the reactivation of HIV-1 provirus at single-cell single-molecule level is necessary to fully comprehend the reactivation of HIV-1 in the reservoirs. Furthermore, since reactivable viruses in the pool of latent reservoirs are rare, combining single-cell imaging techniques with the ability to visualize a large number of reactivated single cells that express both viral RNA and proteins in a pool of uninfected and non-reactivated cells will provide unprecedented information about cell-to-cell variability in reactivation. Here, we describe the single-cell single-molecule RNA-FISH (smRNA-FISH) method to visualize HIV-1 gag RNA combined with the immunofluorescence (IF) method to detect Gag protein to characterize the reactivated cells. This method allows the visualization of subcellular localization of RNA and proteins before and after reactivation and facilitates absolute quantitation of the number of transcripts per cell using FISH-quant. In addition, we describe a high-speed and high-resolution scanning (HSHRS) fluorescence microscopy imaging method to visualize rare and reactivated cells in a pool of non-reactivated cells with high efficiency.

Single cell proteomics analysis of drug response shows its potential as a drug discovery platform.

Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling, nanoscale super-resolution microscopy, advances in single cell RNAseq and proteomics technologies, provide more detailed information about biological processes which were not evident with the analysis of bulk material. This new era of single-cell biology provides a better understanding of such complex biological systems as cancer, inflammation, immunity mechanism and aging processes, and opens the door into the field of drug response heterogeneity. The latest discoveries of cellular heterogeneity gives us a unique understanding of complex biological processes, such as disease mechanism, and will lead to new strategies for better and personalized treatment strategies. Recently, single-cell proteomics techniques that allow quantification of thousands of proteins from single mammalian cells have been introduced. Here we present an improved single-cell mass spectrometry-based proteomics platform called SCREEN (Single Cell pRotEomE aNalysis) for deep and high-throughput single-cell proteome coverage with high efficiency, less turnaround time and with an improved ability for protein quantitation across more cells than previously achieved. We applied this new platform to analyze the single-cell proteomic landscape under different drug treatment over time to uncover heterogeneity in cancer cell response, which for the first time, to our knowledge, has been achieved by mass spectrometry based analytical methods. We discuss challenges in single-cell proteomics, future improvements and general trends with the goal to encourage forthcoming technical developments.

Automatically detecting anchor cells and clustering for scRNA-seq data using scTSNN.

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes.

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

On the compatibility of single-cell microcarriers (nanovials) with microfluidic impedance cytometry.

We investigate for the first time the compatibility of nanovials with microfluidic impedance cytometry (MIC). Nanovials are suspendable crescent-shaped single-cell microcarriers that enable specific cell adhesion, the creation of compartments for undisturbed cell growth and secretion, as well as protection against wall shear stress. MIC is a label-free single-cell technique that characterizes flowing cells based on their electrical fingerprints and it is especially targeted to cells that are naturally in suspension. Combining nanovial technology with MIC is intriguing as it would represent a robust framework for the electrical analysis of single adherent cells at high throughput. Here, as a proof-of-concept, we report the MIC analysis of mesenchymal stromal cells loaded in nanovials. The electrical analysis is supported by numerical simulations and validated by means of optical analysis. We demonstrate that the electrical diameter can discriminate among free cells, empty nanovials, cell-loaded nanovials, and clusters, thus grounding the foundation for the use of nanovials in MIC. Furthermore, we investigate the potentiality of MIC to assess the electrical phenotype of cells loaded in nanovials and we draw directions for future studies.

Insect Insights at the Single-Cell Level: Technologies and Applications.

Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Bariatric surgery induces pancreatic cell transdifferentiation as indicated by single-cell transcriptomics in Zucker diabetic rats.

Bariatric surgery results in rapid recovery of glucose control in subjects with type 2 diabetes mellitus. However, the underlying mechanisms are still largely unknown. The present study aims to clarify how bariatric surgery modifies pancreatic cell subgroup differentiation and transformation in the single-cell RNA level. Male, 8-week-old Zucker diabetic fatty (ZDF) rats with obesity and diabetes phenotypes were randomized into sleeve gastrectomy (Sleeve, n = 9), Roux-en-Y gastric bypass (RYGB, n = 9), and Sham (n = 7) groups. Two weeks after surgery, the pancreas specimen was further analyzed using single-cell RNA-sequencing technique. Two weeks after surgery, compared to the Sham group, the metabolic parameters including fasting plasma glucose, plasma insulin, and oral glucose tolerance test values were dramatically improved after RYGB and Sleeve procedures (p < .05) as predicted. In addition, RYGB and Sleeve groups increased the proportion of pancreatic β cells and reduced the ratio of α cells. Two multiple hormone-expressing cells were identified, the Gcg+/Ppy + and Ins+/Gcg+/Ppy + cells. The pancreatic Ins+/Gcg+/Ppy + cells were defined for the first time, and further investigation indicates similarities with α and β cells, with unique gene expression patterns, which implies that pancreatic cell transdifferentiation occurs following bariatric surgery. For the first time, using the single-cell transcriptome map of ZDF rats, we reported a comprehensive characterization of the heterogeneity and differentiation of pancreatic endocrinal cells after bariatric surgery, which may contribute to the underlying mechanisms. Further studies will be needed to elucidate these results.

SINGLE-CELL AND SPATIAL OMICS FOR MAPPING CELLULAR SENESCENCE IN HEALTH, AGING AND DISEASE

Abstract NIH SenNet consortium aims to dissect the heterogeneity of senescent cells (SnCs) and map their impact on the microenvironment at a single cell resolution and in the spatial tissue context, which requires the implementation of an array of omics technologies to comprehensively identify, characterize, and spatially profile SnCs across tissues in humans and mice. These technologies are broadly categorized into two groups –single cell omics and spatial mapping. To achieve single cell resolution and overcome the scarcity of SnCs, high-throughput single-cell and single-nucleus transcriptomic techniques have become a mainstay tool for surveying tens of thousands of cells to identify transcriptional signatures in rare cell populations, enabling discovery of potential new SnC biomarkers. Novel single cell mass spectrometry methods are developed for unbiased discovery of proteomic signatures of SnCs. A hallmark of SnCs is the senescence-associated secretory phenotype (SASP), which requires the use of proteomics, secretomics, metabolomics and lipidomics, especially SASP-associated extracellular vesicles, for comprehensive characterization of SAPS. High resolution molecular and cellular imaging of gene expression (e.g., MERFISH) or protein markers (e.g., CODEX) is critical for the study of SnCs in the large-scale tissue context. NGS-based spatial omics sequencing is poised to bridge the gap to realize both genome scale and cellular resolution in mapping SnCs in tissue. Novel technologies such as Seq-Scope and Pixel-Seq developed within SenNet further enabled subcellular resolution. SenNet investigators also developed spatially resolved epigenome and multi-omics sequencing techniques to link transcriptional or proteomic phenotype of SnCs to epigenetic mechanism. Further integration with high-resolution imaging makes spatial omics the crucial linchpin in connecting mechanistic underpinnings and molecular signatures with morphological features and spatial distribution. All these are critical for the construction of a map of SnCs and associated niches in the native tissue environment implicated in human health, aging, and disease, which is one of the main goals of the SenNet consortium.

Layer-by-layer coated probiotics with chitosan and liposomes demonstrate improved stability and antioxidant properties in vitro

Probiotics are of increasing interest for their potential health benefits, but their survival and adhesion in the harsh gastrointestinal environment remain a concern. This study explored a single-cell encapsulation technique to enhance probiotic survival and adhesion in the gastrointestinal tract. We encapsulated probiotics in curcumin-loaded liposomes, further coated them with polymers using layer-by-layer techniques. The coated probiotics were evaluated for survival in simulated gastrointestinal conditions, adhesion to colonic mucus, and scavenging of reactive oxygen species (ROS). The results showed that multi-layer encapsulation increased probiotic size at the nanoscale, enhancing their survival in simulated gastrointestinal conditions. Upon reaching the colon, the shedding of the coating coincided with probiotic proliferation. Additionally, the coated probiotics exhibited increased adhesion to colonic mucus. Moreover, the coating acted as a protective barrier for effectively scavenging reactive oxygen radicals, ensuring probiotic survival in inflammatory environments. This study combines the synergistic effects of probiotics and curcumin, underscoring the promise of single-cell encapsulation techniques in improving the efficacy of probiotics for addressing colitis-related diseases.

Quantification of Nitric Oxide in Single Cells Using the Single-Probe Mass Spectrometry Technique.

Nitric oxide (NO) is a small molecule that plays important roles in biological systems and human diseases. The abundance of intracellular NO is tightly related to numerous biological processes. Due to cell heterogeneity, the intracellular NO amounts significantly vary from cell to cell, and therefore, any meaningful studies need to be conducted at the single-cell level. However, measuring NO in single cells is very challenging, primarily due to the extremely small size of single cells and reactive nature of NO. In the current studies, the quantitative reaction between NO and amlodipine, a compound containing the Hantzsch ester group, was performed in live cells. The product dehydro amlodipine was then detected by the Single-probe single-cell mass spectrometry technique to quantify NO in single cells. The experimental results indicated heterogeneous distributions of intracellular NO amounts in single cells with the existence of subpopulations.

Single-cell sequencing identifies inflammation-promoting fibroblast-neutrophil interaction in peri-implantitis.

To reveal the cellular composition and molecular environment of the periodontal and peri-implant inflammatory infiltrates through a single-cell sequencing technique, which may explain the pathological difference between these two diseases. A special focus was placed on the phenotypes and potential roles of neutrophils and fibroblasts in peri-implant/periodontal tissue immunity. High-throughput single-cell transcriptomic profiling of peri-implant tissues from patients with peri-implantitis as well as periodontal tissues from patients with periodontitis and healthy donors was performed. Immunofluorescence analysis was carried out to further validate the identified cell subtypes and their involvement in peri-implantitis and periodontitis. Based on our single-cell resolution analysis, a quantified proportional increase of neutrophil (Neu) subtypes was shown in peri-implantitis. Among these, a predominance of Neutro_CXCR2 was revealed. We also found the involvement of inflammation-promoting fibroblasts as well as a predominance of CXCL8+ fibroblast-CXCR2+ neutrophil interaction in peri-implantitis. Our study indicated that the predominance of CXCL8+ fibroblast-CXCR2+ neutrophil interaction might underline the enhanced host response in peri-implantitis compared with periodontitis. This information offers a molecular basis by which fibroblast and neutrophil subtypes might be diagnostically and therapeutically targeted in peri-implantitis.

Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer’s disease dataset

Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.

Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.

Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.

The unique immune ecosystems in pediatric brain tumors: integrating single-cell and bulk RNA-sequencing.

The significant progress of immune therapy in non-central nervous system tumors has sparked interest in employing the same strategy for adult brain tumors. However, the advancement of immunotherapy in pediatric central nervous system (CNS) tumors is not yet on par. Currently, there is a lack of comprehensive comparative studies investigating the immune ecosystem in pediatric and adult CNS tumors at a high-resolution single-cell level. In this study, we comprehensively analyzed over 0.3 million cells from 171 samples, encompassing adult gliomas (IDH wild type and IDH mutation) as well as four major types of pediatric brain tumors (medulloblastoma (MB), ependymoma (EPN), H3K27M-mutation (DIPG), and pediatric IDH-mutation glioma (P-IDH-M)). Our approach involved integrating publicly available and newly generated single-cell datasets. We compared the immune landscapes in different brain tumors, as well as the detailed functional phenotypes of T-cell and myeloid subpopulations. Through single-cell analysis, we identified gene sets associated with major cell types in the tumor microenvironment (gene features from single-cell data, scFes) and compared them with existing gene sets such as GSEA and xCell. The CBTTC and external GEO cohort was used to analyze and validate the immune-stromal-tumor patterns in pediatric brain tumors which might potentially respond to the immunotherapy. From the perspective of single-cell analysis, it was observed that major pediatric brain tumors (MB, EPN, P-IDH-M, DIPG) exhibited lower immune contents compared with adult gliomas. Additionally, these pediatric brain tumors displayed diverse immunophenotypes, particularly in regard to myeloid cells. Notably, the presence of HLA-enriched myeloid cells in MB was found to be independently associated with prognosis. Moreover, the scFes, when compared with commonly used gene features, demonstrated superior performance in independent single-cell datasets across various tumor types. Furthermore, our study revealed the existence of heterogeneous immune ecosystems at the bulk-RNA sequencing level among different brain tumor types. In addition, we identified several immune-stromal-tumor patterns that could potentially exhibit significant responses to conventional immune checkpoint inhibitors. The single-cell technique provides a rational path to deeply understand the unique immune ecosystem of pediatric brain tumors. In spite of the traditional attitudes of "cold" tumor towards pediatric brain tumor, the immune-stroma-tumor patterns identified in this study suggest the feasibility of immune checkpoint inhibitors and pave the way for the upcoming tide of immunotherapy in pediatric brain tumors.

ScANANSE gene regulatory network and motif analysis of single-cell clusters

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

Single-Cell Mutation Profiling and Alternative Splicing Analysis Reveals Distinct Aberrant Splicing of U2AF1S34 and U2AF1Q157 in Myeloid Malignancies

Introduction: In myeloid neoplasms, spliceosome mutations are found in approximately 50% of patients and are often associated with a poor prognosis. The mutations occur in recurrent hotspot regions, although whether different point mutations affecting the same gene have the same impact remains uncertain. Furthermore, the mechanism through which spliceosome mutations exert a clonal advantage over wild-type cells in the hematopoietic stem/progenitor cell (HSPC) compartment remains poorly understood. Single-cell multiomic techniques combining mutation analysis with RNA-sequencing are a powerful approach to study the effect of specific mutations through an intra-patient comparison of mutant and wild-type HSPCs. Up to now, this approach has not been used to study the effects of specific spliceosome mutations. To this end, we developed experimental and computational pipelines to enable joint genotyping and alternative splicing analysis from the same single cell. We further demonstrated the utility of our approach to identify specific altered splicing events in HSPCs derived from myeloproliferative neoplasm (MPN) patients in association with specific U2AF1 hotspot mutations (S34 and Q157). Methods: &amp;gt;20,000 lineage-CD34+ HSPCs were derived from healthy controls and MPN patients. Among those included for analysis, six patients had JAK2V617F (n=5) or CALR mutations (n=1), of which, two patients had additional U2AF1S34 mutations and three of them, U2AF1Q157 mutations. Parallel single-cell genotyping and short-read RNA-sequencing were performed using TARGET-seq (Rodriguez-Meira et al., 2019). Subsequent alternative splicing analysis was performed using the MARVEL software (https://github.com/wenweixiong/MARVEL). On average, 10,600 splice junctions were detected per cell. Confirmation of candidate alternative splicing events was performed using Nanopore and PacBio long-read sequencing. Results: Single-cell genotyping revealed U2AF1S34 and U2AF1Q157 cells were highly clonal and constituted 85.4% (on average) of the entire HSPC compartment. In contrast, single JAK2V617F mutant patients showed a much more variable clonal burden in the HSPC compartment. We performed differential splicing analysis to identify aberrant splicing events in U2AF1S34 and U2AF1Q157 cells. Differential splicing analysis identified 423 of 2,979 (14.2%) and 1,041 of 6,857 (15.2%) splicing events that were differentially spliced between U2AF1S34 and U2AF1Q157 vs. U2AF1WT cells, respectively (Fig. A). Notably, using this single-cell approach, we successfully recapitulated previously identified U2AF1S34-mediated spliced genes including GNAS and H2AFY, as well as a number of novel aberrantly spliced genes in HSPCs. The repertoire of differentially spliced events was distinct for U2AF1S34 vs. U2AF1Q157 cells. Specifically, only 14 overlapping differentially spliced events were associated with both U2AF1S34 and U2AF1Q157 genotypes. In addition, GSEA analysis identified dysregulation of ribonucleoprotein processes exclusively in U2AF1S34 cells and dysregulation of cell cycle exclusively in U2AF1Q157 cells. This suggests that U2AF1S34 and U2AF1Q157 mutations regulate different molecular pathways in HSPCs and might give rise to clonal expansions through different mechanisms. We next performed long-read sequencing with Nanopore and PacBio to confirm the full isoform sequence of candidate splicing events (Fig. B). Long-read sequencing revealed GNAS to be differentially spliced in U2AF1 S34cells due to a combination of skipped-exon and variable 5‘ and 3‘ untranslated regions. The inclusion of the skipped-exon has been experimentally shown to hyperactivate Gαs protein and downstream ERK/MAPK signalling pathway (Wheeler et al., 2021). Each cell on average expressed two GNAS isoforms (range: 0 to 5). Notably, short-read RNA-sequencing would have limited capacity to detect these isoforms, thus highlighting the advantage of single-cell long-read RNA-sequencing in deconvoluting complex splicing events in the same cell. Conclusion: In this study we demonstrate the proof-of-principle of joint genotyping and alternative splicing analysis at single-cell resolution to identify disease-relevant splicing alterations. We anticipate our approach will enable and accelerate novel biomarker discovery with therapeutic implications in myeloid neoplasms and beyond.

Characterization of Hematopoietic Stem Cell Diversity Using Spatiotemporal Quantitative Phase Imaging

Cells undergo continual transformations, never retaining their original state. Hematopoietic stem cells (HSCs), at the apex of the hematopoietic hierarchy, are not exempt from this dynamic process. In recent years, the advent of single-cell analysis techniques such as scRNAseq has progressively unveiled the heterogeneity within cell populations previously presumed to be homogeneous. However, these techniques are limited to evaluating the cellular state at a single temporal point based on snapshot analysis, thus failing to account for temporal changes and dynamic behavior of cells. Therefore, the true diversity inherent to the ever-changing HSCs remains uncertain. In this study, we comprehensively elucidate HSC diversity, including temporal changes, by integrating our recently established ex vivo HSC expansion techniques (Wilkinson et al., Nature, Becker et al., Cell Stem Cell), single-cell quantitative phase imaging (QPI), and post-transplantation dynamics analysis. For live imaging, HSCs were harvested from hepatic leukemia factor ( Hlf)-tdTomato reporter mice, which specifically marks long-term HSCs. Index sorted single Hlf-tdTomato + HSC were expanded with a single-HSC culture system and monitored by QPI fluorescence time-lapse imaging. Surprisingly, even though the cells were derived from the same HSC fraction, significant variations in mitotic rate, motility, morphological changes, and Hlf expression levels were observed at the single-cell level. Stemness did not decrease stepwise over time, but always exhibited continuous changes. Thus, analysis including the temporal dimension illuminated the infinitely branched nature of HSC diversity. Interestingly, Hlf expression level correlated with the dynamic behavior of HSCs in the expansion culture system. Hlf-high HSCs exhibited a slow division rate, small, rounded morphology and low motility, while Hlf-low HSCs actively divided and differentiated, displayed larger morphological size with low sphericity, and exhibited high motility. Furthermore, spatiotemporal dynamics analysis of Hlf-high/low HSCs after transplantation, employing the highly sensitive in vivo imaging system Akaluc bioluminescence imaging (AkaBLI), revealed that Hlf-high HSCs initiate systemic hematopoiesis slowly, while Hlf-low HSCs expanded quickly, characterized by short-term explosive hematopoiesis in the spleen. As expected, only Hlf-high HSCs had long-term bone marrow reconstitution ability. Thus, based on the expression level of Hlf, it is possible to predict the dynamic behavior of the cells in the expansion medium and in the post-transplant body. Next, we investigated whether Hlf expression level could be predicted based on the kinetic features of cells during HSC ex vivo expansion. Interestingly, multidimensional analysis based on kinetic features extracted from time-lapse imaging showed multiple clusters, each linked to Hlf expression levels. In human HSCs, the dynamic diversity of HSCs was also observed in our recently established ex vivo expansion system (Sakurai et al., Nature). Notably, the kinetic features of human HSCs differed from those of mouse HSCs. Transplantation of human HSCs exhibiting specific morphological characteristics showed long-term reconstitution ability. Thus, classification of HSCs based on kinetic features facilitated enrichment of functional HSCs. This study demonstrates the importance of characterizing cells, including the temporal dimension, and illustrates the existence of infinite dynamic individuality in mouse and human HSCs.

Deconvoluting Clonal and Cellular Architecture in IDH-Mutant Acute Myeloid Leukemia

Introduction Mutations in Isocitrate dehydrogenase 1/2 ( IDH) occur in 20% of acute myeloid leukemias (AML) and frequently co-occur with DNMT3A, NPM1, SRSF2, and NRAS. In clonal hematopoiesis (CH), IDH mutations confer high risk of AML transformation. Recently developed methods linking single cell genotype to single cell transcriptomic profiles and cell lineage offer the opportunity to define the transcriptomic consequences of each mutation across leukemic cell lineages and clonal phylogenies. Here, we develop an integrative single cell laboratory and analytical framework to study the genetic, clonal and cellular heterogeneity of IDH mutated CH and AML, across disease stages (diagnosis and relapse) and deliver a high-resolution map of IDH-mutant AML. Methods Bone marrow or peripheral blood mononuclear samples from 15 AML patients, 2 CH individuals, and 12 unaffected age-matched donors were profiled by single cell RNA-sequencing. For the AML cases, a median of 3 samples per patient were profiled under treatment with IDH inhibitor (IDHi) monotherapy (n=5 patients), IDHi in combination with intensive chemotherapy (NCT02632708, n=6) or in combination with hypomethylating agent (n=2). This dataset comprised 259,610 cells across AML (n=190,133), CH (n=16,705) and normal controls (n=52,772). Genotyping of Transcriptomes (Nam et al., 2019) (for IDH1/2, NPM1, SRSF2, and NRAS mutations) across 6 AML patients enabled genotyping 23% of cells at diagnosis for at least one clone-defining mutation. Results Through meta-analysis of 3,653 diagnostic AML samples profiled by a targeted DNA panel (Papaemmanuil et al., 2016; Tazi et al., 2022), we identified DNMT3A, SRSF2 and NPM1 as critical co-operative mutations in IDH-mutant AML. Amongst the 15 AML patients, 6 had IDH1 mutation, 8 had IDH2 mutations, and 1 had both IDH1 and IDH2. The co-occurring mutations in DNMT3A (n=11 patients), NPM1 (n=5), SRSF2 (n=5) reflected the genomic landscape of IDH-mutant AML. At diagnosis, IDH-mutant AML was enriched in immature hematopoietic stem and progenitor-like blasts. Compared to normal hematopoiesis, IDH-mutant AML blasts upregulated inflammatory response markers including TNFɑ/NFκB-signaling and downregulated proliferation-associated pathways including G2M Checkpoint, and MYC and E2F Targets, and DNA damage response. To investigate how co-mutations modify the IDH-mutant phenotype, we established an integrative single-cell profiling approach to assign single cells to distinct phylogenetic subclones and extract clone-specific transcriptional programs. SRSF2 and NPM1 co-mutations modulated the AML blast phenotype, towards erythroid ( SRSF2) or granulomonocytic lineage ( NPM1). NRAS- or SRSF2-mutant subclones defined distinct gene expression-derived clusters and upregulated proliferation and metabolic pathways. Furthermore, NPM1 co-mutated blasts upregulated KMT2A-target genes. Next, we tracked clonal responses to combination IDHi and chemotherapy using GoT and targeted DNA sequencing. In 4 cases, the ancestral clone, defined by IDH and DNMT3A mutations, retained clonal dominance in both mature and immature myeloid compartments at complete response (median 4 weeks). Conversely, subclones defined by mutations in NPM1, NRAS, or SRSF2, were either reduced or not detectable (limit of detection 2%). In two cases, mutations were not detected after therapy. In patients who relapsed after IDHi monotherapy treatment, blasts upregulated stemness, fatty acid metabolism, OxPHOS, and inflammation at relapse compared to diagnosis. Furthermore, MHC Class II was downregulated at relapse, pointing to immune evasion of AML as a potential relapse mechanism. Finally, we investigated the role of IDH2 mutations at CH. IDH-mutant CH-derived monocytes upregulated inflammation-associated pathways, mirroring the phenotypes observed in IDH-mutant AML at diagnosis. Conclusion We defined how IDH mutations and their frequent co-mutations dysregulated hematopoiesis. We leveraged state-of-the-art single cell techniques to assign leukemic cells to phylogenetic subclones and delineated the role of serially acquired co-mutations in shaping lineage bias and transcriptional programs. Furthermore, we defined genetic, clonal and transcriptional changes at three stages of the AML disease process: diagnosis, remission, and relapse under IDH inhibitor therapy.

Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability.

Existing single-cell bisulfite-based DNA methylation analysis is limited by low DNA recovery, and the measurement of 5hmC at single-base resolution remains challenging. Here, we present a bisulfite-free single-cell whole-genome 5mC and 5hmC profiling technique, named Cabernet, which can characterize 5mC and 5hmC at single-base resolution with high genomic coverage. Cabernet utilizes Tn5 transposome for DNA fragmentation, which enables the discrimination between different alleles for measuring hemi-methylation status. Using Cabernet, we revealed the 5mC, hemi-5mC and 5hmC dynamics during early mouse embryo development, uncovering genomic regions exclusively governed by active or passive demethylation. We show that hemi-methylation status can be used to distinguish between pre- and post-replication cells, enabling more efficient cell grouping when integrated with 5mC profiles. The property of Tn5 naturally enables Cabernet to achieve high-throughput single-cell methylome profiling, where we probed mouse cortical neurons and embryonic day 7.5 (E7.5) embryos, and constructed the library for thousands of single cells at high efficiency, demonstrating its potential for analyzing complex tissues at substantially low cost. Together, we present a way of high-throughput methylome and hydroxymethylome detection at single-cell resolution, enabling efficient analysis of the epigenetic status of biological systems with complicated nature such as neurons and cancer cells.