Biology subjects

Hu, Z.

Publications and source records attributed to Hu, Z..

At least 145 records · Page 8Linked to original sources

bioRxiv2024

Deletion of the endothelial glycocalyx component endomucin leads to impaired glomerular structure and function

BackgroundEndomucin (EMCN), an endothelial-specific glycocalyx component, was found to be highly expressed by the endothelium of the renal glomerulus. We reported an anti-inflammatory role of EMCN and its involvement in the regulation of vascular endothelial growth factor (VEGF) activity through modulating VEGF receptor 2 (VEGFR2) endocytosis. The goal of this study is to investigate the phenotypic and functional effects of EMCN deficiency using the first global EMCN knockout mouse model. MethodsGlobal EMCN knockout mice were generated by crossing EMCN-floxed mice with ROSA26-Cre mice. Flow cytometry was employed to analyze infiltrating myeloid cells in the kidneys. The ultrastructure of the glomerular filtration barrier was examined by transmission electron microscopy, while urinary albumin, creatinine, and total protein levels were analyzed from freshly collected urine samples. Expression and localization of EMCN, EGFP, CD45, CD31, CD34, podocin, albumin, and -smooth muscle actin were examined by immunohistochemistry. Mice were weighed regularly, and their systemic blood pressure was measured using a non-invasive tail-cuff system. Glomerular endothelial cells and podocytes were isolated by fluorescence-activated cell sorting for RNA-seq. Transcriptional profiles were analyzed to identify differentially expressed genes in both endothelium and podocytes, followed by gene ontology analysis of up- and down-regulated genes. Protein levels of EMCN, albumin, and podocin were quantified by Western blot. ResultsEMCN-/- mice were viable with no gross anatomical defects in kidneys. The EMCN-/- mice exhibited increased infiltration of CD45+ cells, with an increased proportion of Ly6GhighLy6Chigh myeloid cells and higher VCAM-1 expression. EMCN-/- mice displayed albuminuria with increased albumin in the Bowmans space compared to the EMCN+/+ littermates. Glomeruli in EMCN-/- mice revealed fused and effaced podocyte foot processes and disorganized endothelial fenestrations. We found no significant difference in blood pressure between EMCN knockout mice and their wild-type littermates. RNA-seq of glomerular endothelial cells revealed downregulation of cell-cell adhesion and MAPK/ERK pathways, along with glycocalyx and extracellular matrix remodeling. In podocytes, we observed reduced VEGF signaling and alterations in cytoskeletal organization. Notably, there was a significant decrease in both mRNA and protein levels of podocin, a key component of the slit diaphragm. ConclusionOur study demonstrates a critical role of the endothelial marker EMCN in supporting normal glomerular filtration barrier structure and function by maintaining glomerular endothelial tight junction and homeostasis and podocyte function through endothelial-podocyte crosstalk.

pathology↗

bioRxiv2024

Endomucin regulates the endothelial cytoskeleton independent of VEGF

The endothelial glycocalyx, lining the apical surface of the endothelium, is involved in a host of vascular processes. The layer contains a network of membrane-bound proteoglycans and glycoproteins. One such glycoprotein is endomucin (EMCN), which our lab has revealed is a modulator of VEGFR2 function. Intravitreal injection of siEMCN into the eyes of P5 mice impairs vascular development. In vitro silencing of EMCN suppresses VEGF-induced proliferation and migration. Signaling pathways that drive cell migration converge on cytoskeletal remodeling. By coupling co-immunoprecipitation with liquid chromatography/mass spectrometry, we identified interactions between EMCN, and proteins associated with actin cytoskeleton organization. The aim of the study was to investigate the influence of EMCN on cytoskeleton dynamics in angiogenesis. EMCN depletion resulted in reduction of F-actin levels, whereas overexpression of EMCN induced membrane protrusions in cells that were rich in stress fibers. The reorganization of the actin filaments did not depend on VEGFR2 signaling, suggesting that EMCN is a potential connection between the cytoskeleton and the glycocalyx.

cell biology↗

bioRxiv2024

Mistargeted retinal axons form synaptically segregated subcircuits in the visual thalamus of albino mice

In albino mice and EphB1 knock out mice, mistargeted retinal ganglion cell (RGC) axons form dense islands of axon terminals in the dorsal lateral geniculate nuclei (dLGN). The formation of these islands of retinal input depends on developmental patterns of spontaneous retinal activity. We reconstructed the microcircuitry of the activity dependent islands and found that the boundaries of the island represent a remarkably strong segregation within retinogeniculate connectivity. We conclude that, when sets of retinal input are established in the wrong part of the dLGN, the developing circuitry responds by forming a synaptically isolated subcircuit within the otherwise fully connected network. The fact that there is a developmental starting condition that can induce a synaptically segregated microcircuit has important implications for our understanding of the organization of visual circuits and for our understanding of the implementation of activity dependent development.

neuroscience↗

bioRxiv2024

A systematic evaluation of dynamic functional connectivity methods using simulation data

Numerous dynamic functional connectivity (dFC) methods have been proposed to study time-resolved network reorganization in rest and task fMRI. However, a comprehensive comparison of their performance is lacking. In this study, we compared the efficacy of seven dFC methods (and their enhanced versions) to track transient network reconfiguration using simulation data. The seven methods include flexible least squares (FLS), dynamic conditional correlation (DCC), general linear Kalman filter (GLKF), multiplication of temporal derivatives (MTD), sliding-window functional connectivity with L1-regularization (SWFC), hidden Markov models (HMM), and hidden semi-Markov models (HSMM). Multiple datasets of non-fMRI-BOLD and fMRI-BOLD signals with predefined covariance structures, signal-to-noise ratio levels, and sojourn time distributions were simulated. We adopted inter-subject analysis to eliminate the effects of signals of non-interest, resulting in enhanced methods: ISSWFC, ISMTD, ISDCC, ISFLS, ISKF, ISHMM, and ISHSMM. Efficacy was defined as the spatiotemporal association between simulated and estimated data. We found that all enhanced dFC methods outperformed their original versions. Efficacies depend on several factors, such as considering the neurovascular effect in simulated data, the covariance structure between two time series, state sojourn distribution, and signal-to-noise ratio levels. These results highlight the importance of selecting appropriate dFC methods in fMRI study.

neuroscience↗

bioRxiv2024

scCancerExplorer: a comprehensive database for interactively exploring single-cell multi-omics data of human pan-cancer

Genomic, epigenomic, and transcriptomic alterations are hallmarks of cancer cells, and are closely connected. Especially, epigenetic regulation plays a critical role in tumorigenesis and progression. The growing single-cell epigenome data in cancer research provide new opportunities for data mining from a more comprehensive perspective. However, there is still a lack of databases designed for interactively exploring the single-cell multi-omics data of human pan-cancer, especially for the single-cell epigenome data. To fill in the gap, we developed scCancerExplorer, a comprehensive and user-friendly database to facilitate the exploration of the single-cell genome, epigenome (chromatin accessibility and DNA methylation), and transcriptome data of 50 cancer types. Five major modules were provided to explore those data interactively, including "integrated multi-omics analysis", "single-cell transcriptome", "single-cell epigenome", "single-cell genome", and "TCGA analysis". By simple clicking, users can easily investigate gene expression features, chromatin accessibility patterns, transcription factor activities, DNA methylation states, copy number variations, and TCGA survival analysis results. Taken together, scCancerExplorer is distinguished from previous databases with rich and interactive functions for exploring the single-cell multi-omics data of human pan-cancer. It bridges the gap between single-cell multi-omics data and the end-users, and will facilitate progress in the field of cancer research. scCancerExplorer is freely accessible via https://bianlab.cn/scCancerExplorer.

cancer biology↗

bioRxiv2024

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

BackgroundVariant interpretation is essential for identifying patients disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). ResultsThe Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. ConclusionsVIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. AvailabilityVIPdb version 2 is available at https://genomeinterpretation.org/vipdb

bioinformatics↗

bioRxiv2024

Label-free visualization of ciliary rootlets in mouse brain

Neuronal primary cilia are important role in brain development, sensory perception and neurogenesis. Rootletin, a fibrous protein composed of coiled-coil motifs, is a major structural component of ciliary rootlets and is essential for understanding ciliary functions. However, the precise mechanisms by which Rootletin influences ciliary dynamics and impacts neuronal function remain largely unknown, primarily due to the challenges in visualizing these structures. Here, we describe a label-free, rapid, and highly sensitive method to visualize Rootletin molecules in brain tissue. This platform integrates a second harmonic generation (SHG) microscope and background reduction by a one-step chemical pretreatment. Additionally, we employ coherent anti-Stokes Raman scattering imaging to simultaneously determine the cellular regions and intracellular locations of SHG signals. By applying this multimodal multiphoton imaging to mouse hippocampus, we found that neuronal ciliary rootlets were found to exhibit highly organized specific intracellular distributions. Moreover, the formation of ciliary rootlets precedes that of primary cilia. These findings highlight the utility of our label-free imaging platform in developmental and neuroscience research, providing a new tool to characterize ciliary dynamics and neuronal function.

biophysics↗

bioRxiv2024

Atomic-scale mechanisms of GDP extraction by SOS1 in KRAS-G12 and KRAS-D12 oncogenes

The guanine exchange factor SOS1 is a crucial node into the positive feedback regulation of the KRAS signaling pathway. Currently, the regulation of KRAS-SOS1 interactions and KRAS downstream effector proteins has become a new hotspot in the development of KRAS-driven cancer therapies. However, the detailed dynamic mechanisms of SOS1-catalyzed GDP extraction and the impact of KRAS mutations remain unknown. Herein, the main mechanisms of GDP extraction from KRAS oncogenes by means of the guanine exchange factor SOS1 are disclosed and described with full details at the atomic-level. For GDP-bound wild-type KRAS, four amino acids (Lys811, Glu812, Lys939 and Glu942) responsible for the catalytic function of SOS1 were identified. With the occurrence of KRAS-G12D mutation, the GDP extraction rate is significantly increased. The molecular interactions behind this phenomenon have been subsequently identified being mainly hydrogen bonding interactions between the mutated residue Asp12 and a positively charged pocket located at the intrinsically disordered region807-818 and composed by Ser807, Trp809, Thr810 and Lys811. Our findings provide new insights into the SOS1-KRAS interactions and facilitate the development of related anti-cancer strategies based on the blockage of the above described mechanisms.

biophysics↗

bioRxiv2024

Immune Checkpoint Blockade Delays Cancer and Extends Survival in Murine DNA Polymerase Mutator Syndromes

Mutations in polymerases Pold1 and Pole exonuclease domains in humans are associated with increased cancer incidence, elevated tumor mutation burden (TMB) and response to immune checkpoint blockade (ICB). Although ICB is approved for treatment of several cancers, not all tumors with elevated TMB respond. Here we generated Pold1 and Pole proofreading mutator mice and show that ICB treatment of mice with high TMB tumors did not improve survival as only a subset of tumors responded. Similarly, introducing the mutator alleles into mice with Kras/p53 lung cancer did not improve survival, however, passaging mutator tumor cells in vitro without immune editing caused rejection in immune-competent hosts, demonstrating the efficiency by which cells with antigenic mutations are eliminated. Finally, ICB treatment of mutator mice earlier, before observable tumors delayed cancer onset, improved survival, and selected for tumors without aneuploidy, suggesting the use of ICB in individuals at high risk for cancer prevention. HighlightsO_LIGermline somatic and conditional Pold1 and Pole exonuclease domain mutations in mice produce a mutator phenotype. C_LIO_LISpontaneous cancers arise in mutator mice that have genomic features comparable to human tumors with these mutations. C_LIO_LIICB treatment of mutator mice with tumors did not improve survival as only a subset of tumors respond. C_LIO_LIIntroduction of the mutator alleles into an autochthonous mouse lung cancer model also did not produce immunogenic tumors, whereas passaging mutator tumor cells in vitro caused immune rejection indicating efficient selection against antigenic mutations in vivo. C_LIO_LIProphylactic ICB treatment delayed cancer onset, improved survival, and selected for tumors with no aneuploidy. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/597960v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@6cd159org.highwire.dtl.DTLVardef@243fd1org.highwire.dtl.DTLVardef@1d9da6borg.highwire.dtl.DTLVardef@192be51_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.

cancer biology↗

bioRxiv2024

Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model

Inspired by the advancements in pre-trained Large Language Models, there has been a surge of studies in the Life Sciences focusing on constructing foundation models with large scale single-cell RNA-seq data. These studies typically involve pre-training a transformer model on large-scale single-cell sequencing data, followed by fine-tuning for a variety of downstream tasks, achieving notable performance. However, these models all share a common short-coming: to utilize the transformer architecture, originally designed for textual data, they artificially impose a sequential structure on genes within cells, simplifying the complex interactions between genes. Furthermore, they focus solely on transcriptomic data, neglecting other relevant biological information. To address these issues, here we introduce Cell-Graph Compass (CGC), the first foundational model that leverages graph structures to model single cells and describes cells from multiple perspectives, including transcriptional profiles, gene text summaries, transcription factor regulatory networks, gene co-expression patterns, and gene positional relationships. By incorporating self-attention mechanisms, we pretrained the model on 50 million human single-cell sequencing data, resulting in a robust digital representation of cells. Extensive downstream experiments demonstrate that our approach can capture meaningful biological knowledge and achieve superior results in various problem scenarios, achieving the state-of-the-art (SOTA).

bioinformatics↗

bioRxiv2024

c-di-GMP modulates ribosome assembly by inhibiting rRNA methylation

Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial secondary messenger, with diverse functions, many of which are yet to be uncovered. Stemming from an Escherichia coli proteome microarray, we found that c-di-GMP bound to 23S rRNA methyltransferases (RlmI and RlmE). rRNA methylation assays showed that c-di-GMP inhibits RlmI activity, thereby modulating ribosome assembly. Based on molecular dynamic simulation and mutagenesis studies, we found that c-di-GMP binds to RlmI at residues R64, R103, G114, and K201. Structural simulation revealed that c-di-GMP quenches RlmI activity by inducing the closure of the catalytic pocket. Furthermore, we revealed that c-di-GMP promotes antibiotic tolerance by regulating RlmI activity, which played a role in antibiotic-resistant strains. Finally, the binding and methylation assays showed that the effect of c-di-GMP on RlmI is conserved, at least in various pathogenic bacteria. This study discovered an unexpected functional role of c-di-GMP in regulating ribosome assembly by inhibiting rRNA methylases. This study identified an unexpected but crucial member among the c-di-GMP effectors. HighlightsO_LIc-di-GMP regulates ribosome assembly in Escherichia coli. C_LIO_LIc-di-GMP inhibits rRNA methylation activity of RlmI by inducing catalytic pocket closure. C_LIO_LIc-di-GMP promotes antibiotic resistance by regulating ribosome assembly. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/597503v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@52baforg.highwire.dtl.DTLVardef@176f86dorg.highwire.dtl.DTLVardef@1afed11org.highwire.dtl.DTLVardef@f53687_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

The theory of massively repeated evolution and full identifications of Cancer Driving Nucleotides (CDNs)

Tumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are Cancer Driving Nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size n = 300 - 1000), point mutations may recur in i out of n patients. This study explores a wide range of mutation characteristics to determine the limit of recurrences (i*) driven solely by neutral evolution. Since no neutral mutation can reach i* = 3, all mutations recurring at i [≥] 3 are CDNs. The theory shows the feasibility of identifying almost all CDNs if n increases to 100,000 for each cancer type. At present, only < 10% of CDNs have been identified. When the full sets of CDNs are identified, the evolutionary mechanism of tumorigenesis in each case can be known and, importantly, gene targeted therapy will be far more effective in treatment and robust against drug resistance.

evolutionary biology↗

bioRxiv2024

On the discovered Cancer Driving Nucleotides (CDNs)-Distributions across genes, cancer types and patients

A central goal of cancer genomics is to identify, in each patient, all the cancer driving mutations. Among them, point mutations are referred to as Cancer Driving Nucleotides (CDNs), which recur in cancers. The companion study shows that the probability of i recurrent hits in n patients would decrease exponentially with i; hence, any mutation with i [≥] 3 hits in the TCGA database is a high-probability CDN. This study characterizes the 50[~]150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows: i) CDNs tend to code for amino acids of divergent chemical properties. ii) At the genic level, far more CDNs (>5-fold) fall on non-canonical than canonical cancer driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs. iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level. iv) Most important, among the 50[~]100 coding region mutations carried by a cancer patient, 5[~]8 CDNs are expected but only 0[~]2 CDNs have been identified at present. This low level of identification has hampered functional test and gene targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.

evolutionary biology↗

bioRxiv2024

Inferring Single-Cell RNA Kinetics from Various Biological Priors

In the context of transcriptional dynamics modeled by ordinary differential equations (ODEs), the RNA level in a single cell is controlled by specific RNA kinetics parameters, which include transcription rate, splicing rate, and degradation rate. Investigating these single-cell RNA kinetics rates is pivotal for understanding RNA metabolism and the heterogeneity of complex tissues. Although metabolic labeling is an effective method to estimate these kinetics rates experimentally, it is not suitable for current large-scale conventional single-cell RNA sequencing (scRNA-seq) data. Moreover, existing methods for scRNA-seq often either neglect certain specific kinetics parameters or use inappropriate ways to fit the parameters. To address these issues, we introduce scRNAkinetics, a parallelized method that fits the kinetics parameters of the ODE for each cell using pseudo-time derived from various biological priors (e.g. cell lineage tree and differentiation potential). This approach allows for the estimation of the relative kinetics of each cell and gene in a scRNA-seq dataset. Validated on simulated datasets, scRNAkinetics can accurately infer the kinetics rates of transcription boosting, multi-branch, and time-dependent RNA degradation systems. Nevertheless, the inferred kinetics trends are concordant with previous studies on metabolic labeling and conventional scRNA-seq datasets. Furthermore, we show that scRNAkinetics can provide valuable insights into different regulatory schemes and validate the coupling between transcription and splicing in RNA metabolism. The open-source implementation of scRNAkinetics is available at https://github.com/poseidonchan/scRNAkinetics.

bioinformatics↗

bioRxiv2024

Repression of PRMT activities sensitize homologous recombination-proficient ovarian and breast cancer cells to PARP inhibitor treatment

Therapeutic epigenetic modulation is currently being evaluated in the clinic to sensitize homologous recombination (HR)-proficient tumors to PARP inhibitors. To broaden its clinical applicability and identify more effective combination strategies, we conducted a drug screen combining PARP inhibitors with 74 well-characterized epigenetic modulators targeting five major classes of epigenetic enzymes. Notably, both type I PRMT inhibitors and PRMT5 inhibitors scored highly in combination efficacy and clinical prioritization. PRMT inhibition significantly enhanced PARP inhibitor-induced DNA damage in HR-proficient ovarian and breast cancer cells. Mechanistically, PRMT suppression downregulates DNA damage repair genes and BRCAness-associated pathways, while also modulating intrinsic innate immune responses within cancer cells. Integrative analysis of large-scale genomic and functional datasets from TCGA and DepMap further supports PRMT1, PRMT4, and PRMT5 as promising therapeutic targets in oncology. Importantly, dual inhibition of PRMT1 and PRMT5 synergistically sensitizes tumors to PARP inhibitors. Collectively, our findings provide strong rationale for the clinical development of PRMT and PARP inhibitor combinations in HR-proficient ovarian and breast cancers.

cancer biology↗

bioRxiv2024

Single-cell phylodynamic inference of tissue development and tumor evolution with scPhyloX

Phylodynamics inference (PI) is a powerful approach for quantifying population dynamics and evolutionary trajectories of natural species based on phylogenetic trees. The emergence of single-cell lineage tracing technologies now enables the reconstruction of phylogenetic trees for thousands of individual cells within a multicellular organism, opening avenues for employing PI methodologies at the cellular level. However, the intricate process of cell differentiation poses challenges for directly applying current PI frameworks in somatic tissues. Here, we introduce a novel computational approach called single-cell phylodynamic explorer (scPhyloX), designed to model structured cell populations in various cell states, by leveraging single-cell phylogenetic trees to infer dynamics of tissue development and tumor evolution. Our comprehensive simulations demonstrate the high accuracy of scPhyloX across various biological scenarios. Application of scPhyloX to three real datasets of single-cell lineage tracing unveils novel insights into somatic dynamics, such as the overshoot of cycling stem cell populations in fly organ development, clonal expansion of multipotent progenitors of hematopoiesis during human aging, and pronounced subclonal selection in early colorectal tumorigenesis. Thus, scPhyloX is an innovative computational method for investigating the development and evolution of somatic tissues.

bioinformatics↗

bioRxiv2024

Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells

Although mitochondrial DNA (mtDNA) variants hold promise as endogenous barcodes for tracking human cell lineages, their efficacy as reliable lineage markers is hindered by the complex dynamics of mtDNA in somatic tissues. Here, we utilized computational modeling and single-cell genomics to thoroughly interrogate the origin and clonal dynamics of mtDNA lineage markers across various biological settings. Our findings revealed the majority of purported "clone informative variants (CIVs)" were pre-existing heteroplamies in the first cell instead of de novo somatic mutations during divisions. Moreover, CIVs demonstrated limited discriminatory power among different lineages during normal development; however, certain CIVs with consistently high allele frequencies proved capable of faithfully labeling cell lineages in scenarios of stringent clonal expansion. Inspired by our simulations, we introduced a lineage informative (LI) score, facilitating the identification of reliable mitochondrial lineage markers across different modalities of single-cell genomic data.

bioinformatics↗