Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

<p dir="ltr">Cancer is a highly heterogeneous disease, both at the genomic and phenotypic level. Single-cell methods have allowed exploration of phenotypic cell states and genomic evolution, but the association between genotype and phenotype remains largely elusive. Activating or deleterious single nucleotide variants (SNVs) can have very clear effects on a phenotype, but copy number variations (CNVs), which are ubiquitous in cancers, are not well understood functionally. Recent advances within ultra-low coverage, single-cell whole genome sequencing (WGS) have enabled high throughput analysis of CNV. However, downstream analysis is still largely dependent on tools developed for high coverage bulk data.</p><p dir="ltr">In <b>paper I</b> we developed a computational method, ASCENT, which enables accurate breakpoint detection, absolute copy number calling, and haplotyping of clonal segments. We showed that by using ASCENT we could find minor subclones not detected in bulk WGS data, and produce accurate high-resolution copy number profiles from ultra-low coverage single-cell WGS.</p><p dir="ltr">By running ASCENT on joint mRNA/WGS data from 57 patients representing six cancer types, we were able to make generalizable inferences about how subclonal genetics affect cell phenotypes (<b>paper III</b>). We found that highly amplified CNVs constrain the phenotype to a much greater degree than lowly amplified whole chromosome aneuploidies. We found that gene dosage is largely tissue-dependent and that oncogenes, such as MYC, are often not sensitive to dosage. We identified a previously underappreciated group of tumors that lack a clear clonal structure, where each cell division leads to two distinct genotypes.</p><p dir="ltr">In <b>paper IV</b> we analyzed the relationship between genotype and phenotype during induction treatment in pediatric acute lymphoblastic leukemia (ALL). We found that while some genotypes have specific phenotypes, during induction treatment cell states shifted toward a more mature B-cell-like state, independent of genomic background. We contrast this to the mechanism which leads to relapse, which always includes additional genomic aberrations, selection on the genetic level, and inter-patient heterogeneity in cell states.</p><p dir="ltr">Single-cell sequencing methods are often used to determine which cell states are responsible for phenotypes found in bulk sequencing. In <b>paper II</b> we investigated the complicated relationship between shear stress and vascular disease in the aorta. We used single-cell sequencing to deconvolve which cell type caused an immune-pro phenotype in AmotL2-depleted mice and found a subset of endothelial cells to be responsible.</p><p dir="ltr">In conclusion, we have charted the transcriptional effect of different genetic aberrations across human cancer. We conclude that specific genomic aberrations can affect the functional phenotype of a cell. We found that dosage effects are largely tissue-dependent and that the amplification of oncogenes is often compensated for on the mRNA-level. We additionally found that induction treatment confers a common cell state in ALL, while relapsed samples had evolved divergent phenotypes during low intensity treatment.</p><h3 dir="ltr">List of scientific papers</h3><p dir="ltr">I. <b>Solrun Kolbeinsdottir</b>*, Vasilios Zachariadis*, Christian Sommerauer, Olli Lohi, Merja Heinäniemi, Martin Enge. Absolute copy number aware CNV calling of sub megabase segments in ultra-low coverage single-cell DNA sequencing data. Nucleic Acids Research, Volume 53, Issue 17, 23 September 2025. <a href="https://doi.org/10.1093/nar/gkaf919" rel="noreferrer" target="_blank">https://doi.org/10.1093/nar/gkaf919</a></p><p dir="ltr">II. Yuanyuan Zhang, Yumeng Zhang, Evelyn Hutterer, Sara Hultin, Otto Bergman, <b>Solrun Kolbeinsdottir</b>, Hong Jin, Maria J Forteza, Daniel F J Ketelhuth, Joy Roy, Ulf Hedin, Martin Enge, Ljubica Matic, Per Eriksson, Lars Holmgren. The VE-cadherin/AmotL2 mechanosensory pathway suppresses aortic inflammation and the formation of abdominal aortic aneurysms. Nature Cardiovascular Research, 2023, 7, 629-644. <a href="https://doi.org/10.1038/s44161-023-00298-8" rel="noreferrer" target="_blank">https://doi.org/10.1038/s44161-023-00298-8</a></p><p dir="ltr">III. <b>Solrun Kolbeinsdottir</b>, Vasilios Zachariadis, Muyi Yang, Luuk Broeils, Christian Sommerauer, Huaitao Cheng, Xinsong Chen, Yingbo Lin, Sampsa Hautaniemi, Johanna Hynninen, Suzanne Egyhazi Brage, Dhifaf Sarhan, Anna Vähärautio, Nikolas Herold, Johan Hartman, Hildur Helgadóttir, Felix Haglund de Flon, Martin Enge. Subclonal copy number alterations and their transcriptional impacts across human cancers using joint single-cell genome and transcriptome sequencing. [Manuscript]</p><p dir="ltr">IV. Vasilios Zachariadis, <b>Solrun Kolbeinsdottir</b>, Jessica Hacheney, Huaitao Cheng, Laura Oksa, Aonghus Naughton, Arghavan Alizadeh, Sanni Moisio, Olli Lohi, Merja Heinäniemi, Martin Enge. Persister states and relapse in childhood leukemia. [Manuscript]</p><p dir="ltr">*These authors contributed equally</p>

Giant Cell Tumor of Bone (GCTB) is a common intermediate tumor, and the specific molecular mechanisms of this disease have not been fully elucidated. It exhibits strong heterogeneity in terms of targets, regulatory mechanisms, cell types, states, and subset distributions in the immune microenvironment. Traditional collective-level analyses cannot accurately reveal these differences. Single-cell sequencing technology is a technique that sequences the genome, transcriptome, and epigenome of diseases at the single-cell level. Single-cell sequencing can utilize a higher pixel resolution to reveal characteristic states of different cell subpopulations, contributing to broadening new perspectives for the study of GCTB heterogeneity. It holds significant value for the precise diagnosis of GCTB, identification of potential immunotherapy targets, and prognosis assessment. This study primarily reviews research related to GCTB, single-cell high-throughput sequencing technology, GCTB immune microenvironment, GCTB heterogeneity, and the construction of GCTB cell maps. It aims to provide theoretical reference for research on single-cell sequencing technology in GCTB and offers a theoretical basis for in-depth exploration of the mechanisms and treatment of GCTB.

scGImpute: A hybrid BiLayer multi-head graph attention-based imputation framework for zero dropout in single-cell sequencing datasets.

Single-cell sequencing for analyzing DNA mutations across the genome in somatic tissues is critically important for studying development, cancer and aging. However, current procedures are prone to artifacts and to date a reliable protocol for single-cell somatic mutation analysis remains to be developed. We address the two largest sources of artifacts, i.e., DNA denaturation-related cytosine deamination and allelic bias-driven whole genome amplification errors. We first reconfigured multiple displacement amplification (MDA) into an efficient protocol for whole genome amplification of single cells without cytosine deamination artifacts, i.e., Single Cell MDA (SCMDA). We then developed a new single-cell SNV caller (SCcaller) that distinguishes real somatic mutations and amplification errors by utilizing a SNP-based localized estimate of allelic amplification bias. The procedure was validated by comparing SCMDA-amplified single cells with unamplified clones derived from single cells from the same population. Using this highly accurate single-cell whole-genome sequencing method we analyzed human B lymphocytes from donors varying in age from birth to over 100 year, studying both genome distribution and functional impact of base substitution mutations. Mutations per cell were found to increase with age from less than 500 in cord blood to over 3,000 in cells from individuals over 100. While overall mutations were randomly distributed across the genome with all chromosomes equally affected, 24 hotspot regions were identified, five of which were part of immunoglobulin variable regions subject to somatic hypermutation. Age-related mutation accumulation was found to be significantly slower in genomic sequences directly involved in cellular function, such as exons and gene regulatory sequences. Still, on average, B lymphocytes from aged individuals contained 3-15 damaging mutations in the transcribed part of the exome identified by RNA-seq, plus 15-50 mutations in transcription factor binding sites identified by ATAC-seq. Analysis of the spontaneous mutation spectra in these normal cells revealed signatures similar to those previously found associated with B cell leukemia and other cancers. These results indicate that cancer risk is already part of age-related mutation accumulation in normal cells. Taken together, our single-cell sequencing method provides a firm foundation for analyzing cellular genetic heterogeneity in normal human tissues. Citation Format: Xiao Dong, Lei Zhang, Moonsook Lee, Alexander Y. Maslov, Jan Vijg. Single-cell whole-genome sequencing reveals somatic mutation signatures in normal somatic cells predictive of cancer later in life [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 440.

Single-Cell Sequencing Reveals a Close Correlation between the Number of MDSCs and the Gene Expression Levels of GPX1 and S100A8/S100A9 in Patients with Myelofibrosis

Single cell sequencing (SCS) was chosen as Method of the Year in 2013 by Nature Methods. It refers to the deep sequencing of DNA or RNA molecules of single cells for the study of genome, transcriptome, noncoding RNA, and protein sequences encompassed within the single cells of interest. SCS possesses a strong potential to exert a great impact on biological research due to its unprecedented opportunity and capability of resolving sequence composition and genetic variations on single cell basis, which can be further integrated to explain the biological phenomena at the population or organismal level. During the past few years, SCS has produced promising results in a number of fields, especially cancer research. Prior to SCS, tissue sequencing could only analyze a tumor mass or a cancer cell population as a whole. With methods for single cell isolation, further powered by molecular level resolution made possible by minute DNA amplification and nucleotide level resolution made possible by next-generation sequencing, SCS technologies are becoming robust approaches for the study of cancer genomics and transcriptomics, and the cancer–normal cell interactions in the microenvironment. SCS analyses of cancer evolution, for example, have shown the capability of being able to empirically infer the driver mutations and map the sequential mutation events during cancer development. Undoubtedly, these novel approaches will produce profound health benefits. SCS will continue to attract more attention, and further expansion of its applications in many biological fields is inevitable. This chapter focuses on SCS methodologies, its existing applications, especially its capability in reconstructing the evolutionary history of cancer progression and in profiling cancer transcriptome, and the potential applications expected to come.

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct “CAF-markers” which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

SummaryTo advance understanding of cellular heterogeneity in disease from single-cell sequencing data, we introduce residual principal-component analysis (ResidPCA), a robust method for identifying cell states that explicitly models cell-type heterogeneity. In simulations, ResidPCA achieved more than 4-fold higher accuracy than conventional PCA and over 3-fold higher accuracy than non-negative matrix factorization (NMF)-based methods in detecting states expressed across multiple cell types. Applied to single-cell RNA sequencing of light-stimulated mouse visual cortex cells, ResidPCA captured stimulus-driven variability with an accuracy more than 5-fold higher than NMF-based approaches. In single-nucleus datasets from an Alzheimer disease cohort, ResidPCA identified 44 chromatin accessibility-based states from single-nucleus ATAC-seq (snATAC-seq) and 42 transcriptional states from single-nucleus RNA-seq. Thirty snATAC-seq states were significantly enriched for Alzheimer disease heritability, often more so than established cell types such as microglia. The snATAC-seq state most significantly enriched for heritability further elucidates a recently implicated neuron-oligodendrocyte-microglial mechanistic axis, linking early amyloid production in neurons and oligodendrocytes with later microglial activation and immune response. These results highlight the ability of ResidPCA to uncover previously hidden biological variation in single-cell data and reveal disease-relevant cell states.

Tumor Heterogeneity-Based Sscr-Score Predicts Prognosis and Reveals Mechanism of SORL1 in Restraining CLL Progression

BackgroundLow back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes.MethodsGene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded.ResultsBy developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD.ConclusionsOur study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD.

BackgroundRenal fibrosis, characterized by the abnormal accumulation of extracellular matrix in renal tissue and progressive loss of kidney function, is posing a significant challenge in clinical treatment. While several therapeutic options exist, effective treatments remain limited. Inonotus obliquus (Chaga), a traditional medicinal mushroom, has shown promising effects in chronic kidney disease (CKD), yet its cellular and molecular mechanisms remain largely unexplored.MethodsWe analysed the chemical composition of Chaga using UPLC-MS and predicted its biological targets using PubChem and Swiss Target Prediction. We used single-cell RNA sequencing to study cellular responses in a mouse model of folic acid-induced renal fibrosis, complemented by spatial transcriptomics to map cellular location patterns. Histological assessment was performed using H&E and Masson trichrome staining.ResultsFor the first time, we employed single-cell RNA sequencing technology to investigate Chaga treatment in renal fibrosis. Histological analysis revealed that Chaga treatment significantly reduced renal tubular damage scores [from 5.00 (5.00, 5.00) to 2.00 (2.00, 2.00), p < 0.05] and decreased collagen deposition area (from 11.40% ± 3.01% to 4.06% ± 0.45%, p < 0.05) at day 14. Through analysis of 82,496 kidney cells, we identified 30 distinct cell clusters classified into eight cell types. Key findings include the downregulation of pro-inflammatory M1 macrophages and upregulation of anti-inflammatory M2 macrophages, alongside decreased T cell responses. Single-cell sequencing revealed differential gene expression in proximal tubular subpopulations associated with reduced fibrosis. Pathway and network pharmacology analyses of 60 identified compounds in Chaga and their 675 predicted targets suggested potential effects on immune and fibrotic pathways, particularly affecting Tregs and NKT cells. Cell-to-cell communication analyses revealed potential interactions between proximal tubular cells, macrophages, and T Cells, providing insights into possible mechanisms by which Chaga may ameliorate renal fibrosis.ConclusionOur study provided new insights into the potential therapeutic effects of Chaga in renal fibrosis through single-cell sequencing analysis. Our findings suggest that Chaga may represent a promising candidate for renal fibrosis treatment, though further experimental validation is needed to establish its clinical application.

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

MotivationAllele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency.Availability and implementationSCSsim is freely available at https://github.com/qasimyu/scssim.Supplementary information Supplementary data are available at Bioinformatics online.

Investigation of human aging at the single-cell level