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15 recordsLinked to original sources
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗
PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗
PubMed2025

Doblin: inferring dominant clonal lineages from high-resolution DNA barcoding time series.

MOTIVATION: The lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, including mutation, drift, and selection. When the population is polyclonal, lineage dynamics also manifest the extent of clonal competition among co-existing mutational variants. If the population exists in a community of other species, the lineage dynamics could also reflect the population's ecological interaction with the rest of the community. Recent advances in high-resolution lineage tracking via DNA barcoding, coupled with next-generation sequencing of bacteria, yeast, and mammalian cells, allow for precise quantification of clonal dynamics in these organisms. RESULTS: In this work, we introduce Doblin, an R suite for identifying dominant barcode lineages based on high-resolution lineage tracking data. We first benchmarked Doblin's accuracy using lineage data from evolutionary simulations, showing that it recovers the clones' identity and relative fitness in the simulation. Next, we applied Doblin to analyze clonal dynamics in laboratory evolutions of Escherichia coli populations undergoing antibiotic treatment and in colonization experiments of the gut microbial community. Doblin's versatility allows it to be applied to lineage time-series data across different experimental setups. AVAILABILITY AND IMPLEMENTATION: Doblin is available on CRAN (https://CRAN.R-project.org/package=doblin) and Github (https://github.com/dagagf/doblin).

DNA Barcoding, Taxonomic↗
PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗
PubMed2025

A cell-state axis underlying colonization in carcinomas with implications for metastasis risk prediction and interception.

Metastasis to the liver drives mortality in pancreatic ductal adenocarcinoma (PDAC), yet mechanisms of colonization remain unclear. Using genomic barcoding, we developed a clonal competition model under immune surveillance, isolating murine PDAC subclones with high or low liver-colonization potential. Combined transcriptome and chromatin-accessibility analyses revealed a distinct "metastatic-potential axis," separate from the normal-to-PDAC and classical-basal axes. We established "MetScore" as a biomarker of this axis. MetScore distinguishes metastases from primary PDAC tumors in patients, predicts outcomes beyond classical-basal classifications, and generalizes across carcinoma subtypes, suggesting conserved colonization mechanisms. High-MetScore PDAC cells preferentially occupy immune cell-enriched niches, suggesting they remodel the metastatic microenvironment. Functional screening identified c-Fos as a positive mediator of colonization and a candidate anti-metastatic target. Collectively, we identify a cell-state axis underpinning PDAC liver colonization, introduce MetScore as a broadly applicable biomarker, and nominate actionable targets for peri-operative therapeutic intervention.

Animals↗
PubMed2025

Paired Single-Cell Transcriptome and DNA Barcode Detection in Zebrafish Using ScarTrace.

ScarTrace is a CRISPR/Cas9-based genetic lineage tracing method that allows for uniquely barcoding the DNA of single cells at a target GFP sequence during developing zebrafish embryos. Single cells from barcoded adult zebrafish can be isolated from various tissues (e.g., marrow, brain, eyes, fins), and their transcriptome and barcode sequences are captured by single-cell cDNA amplification and genomic DNA nested PCR, respectively. Computationally, cell type and barcode identification permit clone tracing and lineage tree reconstruction of tissues to unravel fate decisions during embryogenesis.

Animals↗
PubMed2025

raxtax: a k-mer-based non-Bayesian taxonomic classifier.

MOTIVATION: Taxonomic classification in biodiversity studies is the process of assigning the anonymous sequences of a marker gene (barcode) or whole genomes (metagenomics) to a specific lineage using a reference database that contains named sequences in a known taxonomy. This classification is important for assessing the diversity of biological systems. Taxonomic classification faces two main challenges: first, accuracy is critical as errors can propagate to downstream analysis results; and second, the classification time requirements can limit study size and study design, in particular when considering the constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, novel taxonomic classification tool for barcodes that uses common k-mers between all pairs of query and reference sequences. We also introduce two novel uncertainty scores which take into account the fundamental biases of reference databases. RESULTS: We validate raxtax on three widely-used empirical reference databases and show that it is 2.7-100 times faster than competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibits increasing speedups with growing query and reference sequence numbers compared to existing tools (for 100 000 and 1 000 000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates the taxonomic classification scalability challenge. AVAILABILITY AND IMPLEMENTATION: raxtax is available at https://github.com/noahares/raxtax under a CC-NC-BY-SA license. The scripts and summary metrics used in our analyses are available at https://github.com/noahares/raxtax_paper_scripts. The source code, sequence data, and summarized results of the analyses are available at https://doi.org/10.5281/zenodo.15057027.

Software↗
PubMed2024

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Algorithms↗
PubMed2026

Plant species identification by genome skimming across the vascular plant tree of life.

Accurate species identification is essential for biodiversity conservation and sustainable use, yet standard plant DNA barcoding often fails to achieve species-level resolution. We present a large-scale empirical evaluation of genome skimming as a tool to improve plant species discrimination. Using standardised data from 1969 individuals representing 475 species from 32 genera across major lineages of the vascular plant tree of life, we compare conventional plastid + internal transcribed spacer (ITS) barcodes with genome skimming approaches. Standard barcoding using rbcL, matK, trnH-psbA and ITS resolved about half of species (49.3%), with six genera showing <&#x2009;25% species discrimination. By contrast, genome skimming enabled the recovery of complete plastid genomes, yielding 57.6% species discrimination. It also generated sufficient nuclear genomic data for additional resolution from k-mer analysis, achieving 66.8% species discrimination - an average gain of 17.5% over standard barcodes - while eliminating cases of extreme failure (<&#x2009;25% resolution). The recovery of complete plastomes and ribosomal DNAs from genome skims also ensures backward compatibility with existing barcode datasets. Our results demonstrate that genome skimming provides data that substantially improves species-level resolution across diverse plant lineages and offers a scalable, high-throughput approach for building comprehensive reference resources to support global biodiversity initiatives.

DNA Barcoding, Taxonomic↗
PubMed2025

Cloning and validating systems for high throughput molecular recording.

Molecular recording technologies record and store information about cellular history. Lineage tracing is one form of molecular recording and produces information describing cellular trajectories during mammalian development, differentiation and maintenance of adult stem cell niches, and tumor evolution. Our molecular recorder technology utilizes CRISPR-Cas9 barcode editing to generate mutations in genomically integrated, engineered DNA cassettes, which are read out by single-cell RNA sequencing and used to produce high-resolution lineage trees. Here, we describe optimized cloning and validation procedures to construct the molecular recorder lineage tracing system. We include information on considerations of technology design, cloning procedures, the generation of lineage tracing cell lines, and time course experiments to assess their performance.

Cloning, Molecular↗
PubMed2026

scSNViz: visualization and analysis of cell-specific expressed SNVs.

MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

Software↗
PubMed2026

"Not flying solo": phylogenetic identification and life-cycle insights of larval cestodes in the European flying squid Todarodes sagittatus (Cephalopoda: Ommastrephidae).

The European flying squid Todarodes sagittatus Lamarck is a widely distributed ommastrephid cephalopod in the Northeast Atlantic and Mediterranean Sea, yet its parasite fauna remains poorly documented in the&#xa0;Mediterranean Sea. In this study, two cestode larvae species infecting T. sagittatus from the Algerian coast (Western Mediterranean) were investigated using an integrative approach combining morphological observations and molecular analyses. A total of 63 squids were examined for parasitic infection. Cestodes in plerocercoid stage were detected in 31 individuals (prevalence of 46%), primarily located within the gastrointestinal tract, including the stomach, intestine, and caecum. Morphological features of the larvae were consistent with members of the order Phyllobothriidea but did not allow identification to the species level. Molecular analysis of the D1-D3 region of the 28S rDNA revealed two distinct larval lineages belonging to the genus Crossobothrium Linton, 1889. One lineage showed 100% sequence identity with the adult cestode Crossobothrium dohrnii (Oerley, 1885), a parasite of hexanchid sharks, confirming the identity of these larvae as plerocercoids of C. dohrnii. The second lineage clustered within the Crossobothrium clade but could not be assigned to a known species and is therefore referred to as Crossobothrium sp. The occurrence of these larvae in T. sagittatus, together with ecological data on squid diet and predator-prey relationships, suggests that this cephalopod acts as an intermediate or paratenic host in the life cycle of hexanchid cestodes. Infection likely occurs through predation on crustaceans acting as first intermediate hosts, while transmission to definitive hosts occurs when infected squids are consumed by sharks. These findings provide the first molecular identification of cestode larvae from T. sagittatus in the Mediterranean and highlight the important role of ommastrephid squids in the trophic transmission of elasmobranch parasites in pelagic ecosystems.

Crossobothrium↗
PubMed2024

Whole-genome sequences reveal zygotic composition in chimeric twins.

While most dizygotic twins have a dichorionic placenta, rare cases of dizygotic twins with a monochorionic placenta have been reported. The monochorionic placenta in dizygotic twins allows in utero exchange of embryonic cells, resulting in chimerism in the twins. In practice, this chimerism is incidentally identified in mixed ABO blood types or in the presence of cells with a discordant sex chromosome. Here, we applied whole-genome sequencing to one triplet and one twin family to precisely understand their zygotic compositions, using millions of genomic variants as barcodes of zygotic origins. Peripheral blood showed asymmetrical contributions from two sister zygotes, where one of the zygotes was the major clone in both twins. Single-cell RNA sequencing of peripheral blood tissues further showed differential contributions from the two sister zygotes across blood cell types. In contrast, buccal tissues were pure in genetic composition, suggesting that in utero cellular exchanges were confined to the blood tissues. Our study illustrates the cellular history of twinning during human development, which is critical for managing the health of chimeric individuals in the era of genomic medicine.

Humans↗