Copy number variants calling for single cell sequencing data by multi-constrained optimization

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.

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.

The accurate estimation of cell type proportions in tissues is crucial for various downstream analyses. With the increasing availability of single-cell sequencing data, numerous deconvolution methods that use single-cell RNA sequencing data as a reference have been developed. However, a unified understanding of how these deconvolution approaches perform in practical applications is still lacking. To address this, we systematically assessed the accuracy and robustness of nine deconvolution methods that use single-cell RNA sequencing data as a reference, evaluating them on real bulk data with cell proportions verified through flow cytometry, as well as simulated bulk data generated from five single-cell RNA sequencing datasets. Our study highlights the importance of several factors-including reference dataset construction strategies, dataset size, cell type subdivision, and cell type inconsistency-on the accuracy and robustness of deconvolution results. We also propose a set of recommended guidelines for software users in diverse scenarios.

Meningiomas are the most common tumours that primarily arise in the central nervous system, but their intratumoural heterogeneity has not yet been thoroughly studied. We aimed to investigate the transcriptome characteristics and biological properties of ECM-remodeling meningioma cells. Single-cell RNA sequencing (ScRNA-seq) data from meningioma samples were acquired and used for analyses. We conducted comprehensive bioinformatics analyses, including screening for differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) term enrichment analyses, Gene Set Enrichment Analysis (GSEA), protein-protein interaction (PPI) analysis, and copy number variation (CNV) analysis on single-cell sequencing data from meningiomas. Eighteen cell types, including six meningioma subtypes, were identified in the data. ECM-remodeling meningioma cells (MGCs) were mainly distributed in brain-tumour interface tissues. KEGG and GO enrichment analyses revealed that 908 DEGs were mainly related to cell adhesion, extracellular matrix organization, and ECM-receptor interaction. GSEA analysis demonstrated that homophilic cell adhesion via plasma membrane adhesion molecules was significantly enriched (NES = 2.375, P < 0.001). CNV analysis suggested that ECM-remodeling MGCs showed considerably lower average CNV scores. ECM-remodeling MGCs predominantly localized at the brain-tumour interface area and adhere stably to the basement membrane with a lower degree of malignancy. This study provides novel insights into the malignancy of meningiomas.

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.

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.

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.

Objectives Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiomyopathy and immune infiltration is considered an indispensable factor involved in its pathogenesis. In this study, we attempted to combine bulk sequencing and single-cell sequencing to map the immune infiltration-related genes in hypertrophic cardiomyopathy. Methods The GSE36961, GSE160997, and GSE122930 datasets were obtained from the Gene Expression Omnibus database. The compositional patterns of the 18 types of immune cell fraction and pathway enrichment score in control and HCM patients were estimated based on the GSE36961 cohort using xCell algorithm. The Weighted Gene Coexpression Network Analysis (WGCNA) was performed to identify genes associated with immune infiltration for hypertrophic cardiomyopathy. The area under the curve (AUC) value was obtained and used to evaluate the discriminatory ability of common immune-related DEGs. “NetworkAnalyst” platform was used to identify TF-gene and TF-miRNA interaction with identified common genes. Heat map was used to determine the association between common DEGs and various immune cells. Results Immune infiltration analysis by the xCell algorithm showed a higher level of CD8+ naive T cells, CD8+ T cells, as well as a lower level of activated dendritic cells (aDC), dendritic cells (DC), immature dendritic cells (iDC), conventional dendritic cells (cDC), macrophages, M1 macrophages, monocytes, and NKT cell in HCM compared with the control group in GSE36961 dataset. aDC, macrophages, and M1 macrophages were the top three discriminators between HCM and control groups with the area under the curve (AUC) of 0.907, 0.867, and 0.941. WGCNA analysis showed that 1258 immune-related genes were included in four different modules. Of these modules, the turquoise module showed a pivotal correlation with HCM. 13 common immune-related DEGs were found by intersecting common DEGs in GSE36961 and GSE160997 datasets with genes from the genes in turquoise module. 5 hub immune-related genes (S100A9, TYROBP, FCER1G, CD14, and S100A8) were identified by protein interaction network. Through analysis of single-cell sequencing data, S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory macrophage cells in the heart immune microenvironment while Cd14 was expressed by infiltrated M1 proinflammatory macrophage cells and M2 macrophages in transverse aortic constriction (TAC) mice at 1 week. Higher M2 macrophage and M1 proinflammatory macrophage infiltration as well as lower Ccr2-M1 proinflammatory macrophage and dendritic cells were shown in TAC 1week mice compared with sham mice. Conclusions There was a difference in immune infiltration between HCM patients and normal groups. aDC, macrophages, and M1 macrophages were the top three discriminator immune cell subsets between HCM and control groups. S100A9, TYROBP, FCER1G, CD14, and S100A8 were identified as potential biomarkers to discriminate HCM from the control group. S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory cells in the heart immune microenvironment while Cd14 was expressed by M2 macrophages in transverse aortic constriction (TAC) mice at 1 week.

Class I family enzymes, histone deacetylases 1 and 2 (HDAC1,2), are targets of FDA-approved small molecule inhibitors currently used in cancer therapy. HDAC inhibitors impair DNA repair and replication in cancer cells to trigger genotoxic stress and cause cell death. How HDAC1,2 function in genome maintenance processes, DNA replication and repair, in mammalian cells is far from fully understood. Using small molecules to selectively inhibit HDAC1,2 activities, we have systematically defined their direct functions in DNA replication and repair. Our studies show that HDAC1,2 inhibition reduces replication fork velocity and activates the replication stress associated DNA damage independent of its effect on transcription. HDAC1,2 inhibition increases histone H4K16 acetylation (H4K16ac) and nuclease sensitivity of nucleosomes associated with newly replicated DNA, suggesting that HDAC1,2 are required for chromatin compaction during DNA replication. Thus, mechanistically, HDAC1,2 activities are required for maintaining nascent chromatin structure and dynamics required for efficient progression of the replication fork. In addition to H4K16ac, we have found that HDAC1,2 inhibition activities increases H3K27ac and H4K91ac present on the histone tail and core regions, respectively. Increase in these histone acetyl marks perturbs the acetyl-methyl or the acetyl-ubiquityl switch during DNA repair leading to impaired EZH2-mediated H3K27me3-dependent DNA repair signaling and the H4K91ubiquityl-dependent 53BP1-mediated DNA repair signaling in chemoresistant EZH2 gain-of-function diffuse large B-cell lymphoma cells. We are currently investigating the therapeutic benefits of HDAC1,2 inhibition in mouse models of aggressive chemoresistant diffuse large B-cell lymphoma and in primary patient samples. Overall, our studies have uncovered novel functions for HDAC1,2 in genome maintenance processes and provide insight into the mode-of-action of HDAC1,2 inhibitors in overcoming chemoresistance in cancer cells (via targeting the DNA repair process) and in inducing DNA damage and death in the rapidly cycling cancer cells (via targeting DNA replication). Note: This abstract was not presented at the conference. Citation Format: Danielle Johnson, Simon Jones, Steven Quayle, Jeff Shearstone, Srividya Bhaskara. Inhibition of histone deacetylases 1 and 2 (HDAC1,2) perturbs DNA replication and DNA repair in cancer cells: Implications in mechanism-based therapeutic strategies. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr B29.

The development of selective anticancer agents remains a central objective in medicinal chemistry. Here, we report the synthesis, structural characterization, and biological evaluation of the first metal complex of a quinoline-based thiazolyl-hydrazone ligand [(E)-2-(8-(quinolin-2-ylmethylene)hydrazinyl)-4-(4-tolyl)-1,3-thiazole)] (HL). Obtained octahedral Zn(II) complex [Zn(HL)2](NO3)2, (1), is based on two NNN tridentate coordinated ligands. In vitro cytotoxicity assays revealed that the complex exhibits potent and selective activity against lung adenocarcinoma A-549 and colorectal carcinoma HCT-116, while sparing non-tumorigenic human embryonic kidney HEK-293 cells. Mechanistic studies demonstrated that the complex induces S-phase arrest, inhibits DNA synthesis, and triggers significant DNA damage as indicated by increased p-γH2AX expression. These effects were accompanied by mitochondrial depolarization, caspase-3/7 activation, and phosphatidylserine externalization, confirming apoptosis via the intrinsic pathway. The compound further inhibited tumor growth in 3D spheroids and the chick chorioallantoic membrane model without overt toxicity. Compared to the free ligand and reference chemotherapeutics, the zinc complex showed improved efficacy and selectivity. These results highlight the potential of metal coordination to enhance the biological activity of hybrid ligands, supporting further development of this class of compounds as selective anticancer agents.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27051-4.

Platinum compounds are widely used antitumor agents known to interfere with DNA function by forming DNA crosslinks and DNA-protein crosslinks. Because of their electrophilicity, platinum compounds can interact with nucleophilic residues of all macromolecules. Consequently, this cross-linking inhibits DNA replication in cancer cells. Immunogenic and immunomodulating effects have been ascribed to platinum drugs, with differences and similarities among cisplatin, carboplatin and oxaliplatin. On the one hand, cisplatin is generally unable to induce immunogenic cell death; on the other hand, oxaliplatin appears to be a good inducer, thanks to its capability to efficiently trigger calreticulin exposure to the tumor cell plasma membrane. Conversely, cisplatin, carboplatin and oxaliplatin can relieve immunosuppressive networks e.g., by decreasing PDL-1 and PDL-2 in dendritic and tumor cells. Such drugs are also capable of modulating MHC molecules via IFN-β production and T-cell mediated lysis. The concentrations appear to be key in determining the immunomodulatory properties of these cytotoxic agents, with low in vivo doses usually playing stimulatory effects. As predicted from preclinical models, supportive results have emerged from clinical studies, particularly those based on chemotherapeutic regimens of platinum compounds combined with immunotherapeutics. Future therapeutic interventions are expected to benefit from a better definition of the molecular effects of platinum compounds on the immune system.

Doxorubicin-loaded micelles in tumor cell-specific chemotherapy

BackgroundDepletion of replication factors often causes cell death in cancer cells. Depletion of Cdc7, a kinase essential for initiation of DNA replication, induces cancer cell death regardless of its p53 status, but the precise pathways of cell death induction have not been characterized.Methodology/Principal FindingsWe have used the recently-developed cell cycle indicator, Fucci, to precisely characterize the cell death process induced by Cdc7 depletion. We have also generated and utilized similar fluorescent cell cycle indicators using fusion with other cell cycle regulators to analyze modes of cell death in live cells in both p53-positive and -negative backgrounds. We show that distinct cell-cycle responses are induced in p53-positive and -negative cells by Cdc7 depletion. p53-negative cells predominantly arrest temporally in G2-phase, accumulating CyclinB1 and other mitotic regulators. Prolonged arrest at G2-phase and abrupt entry into aberrant M-phase in the presence of accumulated CyclinB1 are followed by cell death at the post-mitotic state. Abrogation of cytoplasmic CyclinB1 accumulation partially decreases cell death. The ATR-MK2 pathway is responsible for sequestration of CyclinB1 with 14-3-3σ protein. In contrast, p53-positive cancer cells do not accumulate CyclinB1, but appear to die mostly through entry into aberrant S-phase after Cdc7 depletion. The combination of Cdc7 inhibition with known anti-cancer agents significantly stimulates cell death effects in cancer cells in a genotype-dependent manner, providing a strategic basis for future combination therapies.ConclusionsOur results show that the use of Fucci, and similar fluorescent cell cycle indicators, offers a convenient assay system with which to identify cell cycle events associated with cancer cell death. They also indicate genotype-specific cell death modes induced by deficient initiation of DNA replication in cancer cells and its potential exploitation for development of efficient cancer therapies.

ATR is essential to protect genome integrity during DNA replication in cancer cells. In this first disclosure, Roulston and colleagues identify RP-3500 (NCT04497116), a highly potent and selective oral ATR inhibitor with efficacy as a monotherap. and in combination with PARP inhibitors in several xenograft models. Preclinically, intermittent dosing of RP-3500 maintains efficacy without inducing anemia, a dose-limiting toxicity observed clinically for ATR inhibitors. Furthermore, concurrent administration of RP-3500 with PARP inhibitors on an intermittent schedule provides greater efficacy and reduced toxicity compared to sequential administration, providing an opportunity to maximize clinical benefit for this class of anti-cancer agent.Hampton and colleagues describe an approach for precision medicine of platinums in the treatment of triple-negative breast cancer (TNBC) through exploitation of the glycosaminoglycan (sGAG) expression of the tumor. The in vivo accumulation and antitumor efficacy of the paradigmatic polynuclear platinum complex Triplatin (BBR3464) in TNBC cells positively correlated with sGAG levels, representing a discrete mechanism of selective tumor accumulation not shared by the mononuclear agents. The paper validates the exploitation of sGAG profile as biomarker predicting response to therapy suggesting personalized approaches for TNBC patients—enhancement of neoadjuvant and adjuvant treatment will likely improve clinical outcome of TNBC.After decades of slow progress in the clinic, antibody drug conjugates (ADCs) have catapulted into the spotlight with 7 approvals in the past 3 years. One theme of recent approvals is the use of ‘bystander’ payloads capable of diffusing into adjacent cells after antibody targeting. However, the distribution of these payloads after release is unclear. This study quantifies and compares the tissue penetration and efficacy of all 3 classes of ADC payloads used in the clinic in a 3D tissue culture system. These results provide the necessary data to design more efficient next generation ADCs for clinical efficacy.There is a critical need for treatment modalities that improve immune checkpoint blockade responses in ovarian cancer. Radiation therapy has been shown to synergize with PD-1/PD-L1 blockade in some cancers but has not been utilized in ovarian cancer due to the toxicity associated with conventional abdominopelvic irradiation. Eggold and Chow et al. demonstrate that ultra-high dose rate FLASH irradiation combined with PD-1 inhibition enhances intratumoral T-cell infiltration and tumor control with minimal toxicity in preclinical models of ovarian cancer. These data suggest that FLASH irradiation may improve the therapeutic efficacy of checkpoint inhibition in the treatment of ovarian cancer.

BackgroundVariation in DNA copy number, due to gains and losses of chromosome segments, is common. A first step for analyzing DNA copy number data is to identify amplified or deleted regions in individuals. To locate such regions, we propose a circular binary segmentation procedure, which is based on a sequence of nested hypothesis tests, each using the Bayesian information criterion.ResultsOur procedure is convenient for analyzing DNA copy number in two general situations: (1) when using data from multiple sources and (2) when using cohort analysis of multiple patients suffering from the same type of cancer. In the first case, data from multiple sources such as different platforms, labs, or preprocessing methods are used to study variation in copy number in the same individual. Combining these sources provides a higher resolution, which leads to a more detailed genome-wide survey of the individual. In this case, we provide a simple statistical framework to derive a consensus molecular signature. In the framework, the multiple sequences from various sources are integrated into a single sequence, and then the proposed segmentation procedure is applied to this sequence to detect aberrant regions. In the second case, cohort analysis of multiple patients is carried out to derive overall molecular signatures for the cohort. For this case, we provide another simple statistical framework in which data across multiple profiles is standardized before segmentation. The proposed segmentation procedure is then applied to the standardized profiles one at a time to detect aberrant regions. Any such regions that are common across two or more profiles are probably real and may play important roles in the cancer pathogenesis process.ConclusionsThe main advantages of the proposed procedure are flexibility and simplicity.