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PubMed2025

ChemPerturb-seq screen identifies a small molecule cocktail enhancing human beta cell survival after subcutaneous transplantation.

Traditional chemical screens have focused on a single assay per screen, making them labor intensive and costly. Here, we combined a chemical screen with single-cell RNA sequencing (scRNA-seq) to perform Chemical Perturb-seq (ChemPerturb-seq), enabling a systematic analysis of the molecular changes of human beta cells upon individual small molecule treatments. Using this platform, we performed an in vivo barcoded screen and discovered a small molecule cocktail, including beta-lipotropin 61-91, insulin growth factor-1, and prostaglandin E2, with which preconditioning human beta cells and primary islets significantly enhanced function and survival when transplanted subcutaneously to female, but not to male, mice. We identified two additional molecules, serotonin and histamine, that promote islet function when transplanted subcutaneously to male mice using ChemPerturb-seq. Such small molecule cocktails could be applied to improve the current FDA-approved islet transplantation procedure. Finally, we developed an artificial intelligence (AI)-powered website, ChemPerturbDB, which provides user-friendly open access analysis of the extensive ChemPerturb-seq dataset.

Humans↗
PubMed2024

Mudskipper detects combinatorial RNA binding protein interactions in multiplexed CLIP data.

The uncovering of protein-RNA interactions enables a deeper understanding of RNA processing. Recent multiplexed crosslinking and immunoprecipitation (CLIP) technologies such as antibody-barcoded eCLIP (ABC) dramatically increase the throughput of mapping RNA binding protein (RBP) binding sites. However, multiplex CLIP datasets are multivariate, and each RBP suffers non-uniform signal-to-noise ratio. To address this, we developed Mudskipper, a versatile computational suite comprising two components: a Dirichlet multinomial mixture model to account for the multivariate nature of ABC datasets and a softmasking approach that identifies and removes non-specific protein-RNA interactions in RBPs with low signal-to-noise ratio. Mudskipper demonstrates superior precision and recall over existing tools on multiplex datasets and supports analysis of repetitive elements and small non-coding RNAs. Our findings unravel splicing outcomes and variant-associated disruptions, enabling higher-throughput investigations into diseases and regulation mediated by RBPs.

RNA-Binding Proteins↗
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↗
PubMedDate not supplied

[Direct collection of information during blood donation in blood mobiles].

Direct data storage, in the presence of the donor, ensures a realistic reliability and erases any deferred interpretation of the essential information for a good donation identification. Computers can help relieve this restraint. Indeed the evolution of microcomputers (increased capacity and power, decreased weight and volume, and good performance in any use makes them more and more accurate. Associated use of barcodes and microcomputers gives a nonmanual non-human line of words: donor-donation-analysis-blood products-patient. We found, working that way, the following advantages: Information secured. No deferred inputs. Instart comparison of the information on the donor's past history. Accurate donation-donor connection. Such a system represents one more step towards a continuous line of treatment from Donor to Patient in regard of Blood Transfusion Safety.

Blood Donors↗
PubMed1986

The NCI Drug Information System. 4. Inventory and Shipping Modules.

The Inventory/Shipping package of the NCI Drug Information System (DIS) is designed to support all inventory and shipping operations associated with the testing by the NCI of large numbers of chemicals for anticancer activity. Two major databases, an Inventory database and a Shipping History database, contain all of the data associated with these operations. Software that supports the operations in an online interactive manner also provides for the accessing and updating of these databases as necessary. Special hardware in the form of barcode reader/printers and digital balances is also interfaced to the system to improve the efficiency of the operations.

Antineoplastic Agents↗
PubMed2025

Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network.

TP53, the most frequently mutated gene in human cancer, encodes a transcriptional activator that induces myriad downstream target genes. Despite the importance of p53 in tumor suppression, the specific p53 target genes important for tumor suppression remain unclear. Recent studies have identified the p53-inducible gene Zmat3 as a critical effector of tumor suppression, but many questions remain regarding its p53-dependence, activity across contexts, and mechanism of tumor suppression alone and in cooperation with other p53-inducible genes. To address these questions, we used Tuba-seqUltra somatic genome editing and tumor barcoding in a mouse lung adenocarcinoma model, combinatorial in vivo CRISPR/Cas9 screens, meta-analyses of gene expression and Cancer Dependency Map data, and integrative RNA-sequencing and shotgun proteomic analyses. We established Zmat3 as a core component of p53-mediated tumor suppression and identified Cdkn1a as the most potent cooperating p53-induced gene in tumor suppression. We discovered that ZMAT3/CDKN1A serve as near-universal effectors of p53-mediated tumor suppression that regulate cell division, migration, and extracellular matrix organization. Accordingly, combined Zmat3-Cdkn1a inactivation dramatically enhanced cell proliferation and migration compared to controls, akin to p53 inactivation. Together, our findings place ZMAT3 and CDKN1A as hubs of a p53-induced gene program that opposes tumorigenesis across various cellular and genetic contexts.

Animals↗
PubMed2026

Plasticity of extrachromosomal DNA segregation during drug adaptation.

Uneven segregation during mitosis is a striking feature of extrachromosomal DNA (ecDNA). Because ecDNA lacks a centromere, it is thought to segregate stochastically, generating intratumoral heterogeneity in genomic copy number. Drug treatment can readily change ecDNA copy number, enabling cells to acquire drug resistance, yet whether these changes reflect static selection of pre-existing clones or active reconfiguration under stress remains unresolved. To address this, we develop a high-throughput framework combining single-cell DNA sequencing with cellular barcoding for clonal tracking. Single-cell cloning reveals that not all clones exhibit identical segregation modes even under drug-free conditions. Under treatment, resistant populations do not simply arise from pre-existing clones with favorable ecDNA states; instead, some clones actively reconfigure their segregation behavior to generate resistant cells. Thus, although ecDNA generally segregates stochastically, it can undergo nonrandom, actively regulated segregation under drug stress, raising the possibility of therapeutically targeting ecDNA segregation mechanisms to counteract adaptive resistance.

Extrachromosomal DNA↗
PubMed2025

Efficient and multiplexed somatic genome editing with Cas12a mice.

Somatic genome editing in mouse models has increased our understanding of the in vivo effects of genetic alterations. However, existing models have a limited ability to create multiple targeted edits, hindering our understanding of complex genetic interactions. Here we generate transgenic mice with Cre-regulated and constitutive expression of enhanced Acidaminococcus sp. Cas12a (enAsCas12a), which robustly generates compound genotypes, including diverse cancers driven by inactivation of trios of tumour suppressor genes or an oncogenic translocation. We integrate these modular CRISPR RNA (crRNA) arrays with clonal barcoding to quantify the size and number of tumours with each array, as well as the impact of varying the guide number and position within a four-guide array. Finally, we generate tumours with inactivation of all combinations of nine tumour suppressor genes and find that the fitness of triple-knockout genotypes is largely explainable by one- and two-gene effects. These Cas12a alleles will enable further rapid creation of disease models and high-throughput investigation of coincident genomic alterations in vivo.

Animals↗
PubMed2025

Fluctuating DNA methylation tracks cancer evolution at clinical scale.

Cancer development and response to treatment are evolutionary processes1,2, but characterizing evolutionary dynamics at a clinically meaningful scale has remained challenging3. Here we develop a new methodology called EVOFLUx, based on natural DNA methylation barcodes fluctuating over time4, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterized lymphoid cancer samples spanning a broad spectrum of diseases and show that initial tumour growth rate, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe faster initial tumour growth in more aggressive disease subtypes, and that evolutionary histories are strong independent prognostic factors in two series of chronic lymphocytic leukaemia. Using EVOFLUx for phylogenetic analyses of aggressive Richter-transformed chronic lymphocytic leukaemia samples detected that the seed of the transformed clone existed decades before presentation. Orthogonal verification of EVOFLUx inferences is provided using additional genetic data, including long-read nanopore sequencing, and clinical variables. Collectively, we show how widely available, low-cost bulk DNA methylation data precisely measure cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.

Humans↗
PubMed2026

Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.

Spatial long-read technologies are increasingly common but usually lack single-cell resolution. This leaves unanswered whether spatially variable isoforms reflect variability within one cell type or differences in region-specific cell-type composition. Here, we developed Spl-ISO-Seq2 (500-nm resolution) and accompanying software, Spl-IsoQuant-2 and Spl-IsoFind, enabling long-read sequencing of >450 million barcodes versus 80,000 previously. Applying this to the adult mouse brain, we compared differential isoform abundance between known regions and spatial isoform patterns independent of predefined regions. Both identified overlapping hits, for example, Rps24 in oligodendrocytes. For known Snap25 spatial isoform variation, we show that it occurs in excitatory neurons. The region-agnostic approach also uncovered patterns missed by region-based comparisons, for example, for Ighm. Notably, many spatial isoform signals are not driven by cell-type composition alone. Finally, our software is applicable to many spatial and single-cell protocols, demonstrating reproducibility between platforms (for example, Visium HD/Stereo-seq). Overall, our experimental/analytical methods enable a submicron-resolution-isoform view and open avenues for spatial isoform disease research.

Animals↗
PubMed1991

Development of the Abbott MATRIX Aero assay for the measurement of specific IgE.

An enzyme immunoassay has been developed for the quantitation of specific immunoglobulin E (IgE) in human serum to a panel of allergens. The assay system, called the Abbott MATRIX Aero, includes an instrument, reagents and test cell disposables. Each test cell contains fourteen airborne allergens individually localized on a nitrocellulose solid phase. Individual calibration curves for each allergen are established by the manufacturer and included in barcode form with each test kit. Stable factory calibration eliminates the need to establish a calibration curve with each assay run. The instrument automatically incubates, washes, and reads the test cell and prints each result, which ensures assay reproducibility and provides ease-of-use. Analysis of test results shows good agreement with another in vitro assay for specific IgE. The Abbott MATRIX Aero is a sensitive, reproducible and easy-to-use system for the measurement of specific IgE to a panel of fourteen allergens simultaneously using a single, small volume of serum.

Allergens↗
PubMed2026

MHASS: Microbiome HiFi Amplicon Sequencing Simulator.

SUMMARY: Microbiome HiFi Amplicon Sequence Simulator (MHASS) creates realistic synthetic PacBio HiFi amplicon sequencing datasets for microbiome studies, by integrating genome-aware abundance modeling, realistic dual-barcoding strategies, and empirically derived pass-number distributions from actual sequencing runs. MHASS generates datasets tailored for rigorous benchmarking and validation of long-read microbiome analysis workflows, including ASV clustering and taxonomic assignment. AVAILABILITY AND IMPLEMENTATION: Implemented in Python with automated dependency management, the source code for MHASS is freely available at https://github.com/rhowardstone/MHASS along with installation instructions. Our code is also published on Zenodo at https://doi.org/10.5281/zenodo.17486364. The data underlying this article are available on GitHub at https://github.com/rhowardstone/MHASS_evaluation/.

Software↗
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

RUMINA: high-throughput deduplication of unique molecular identifiers for amplicon and whole-genome sequencing with enhanced error correction.

MOTIVATION: Unique molecular identifiers (UMIs) are widely used in next-generation sequencing to enable accurate molecular counting and error correction. However, challenges remain in accurately collapsing UMI clusters, especially when read counts are low or sparse read clusters arise from barcode sequencing errors. RESULTS: We present RUMINA, a Rust-based pipeline for UMI-aware deduplication and error correction, optimized for both amplicon and shotgun sequencing. RUMINA supports multiple UMI cluster strategies, alongside majority-rule read selection independent of mapping quality, as well as discrete handling of 1-2 read clusters, paired-end merging, and read-length stratification. Benchmarking using simulated HIV population sequencing data and real-world iCLIP and TCR datasets showed that RUMINA improves ultra-low frequency SNV detection (0.01%-1%), reduces false positives, enhances reproducibility, and processes sequencing data up to 10-fold faster than existing tools. By integrating UMI- and sequence-level correction in a high-performance framework, RUMINA offers a fast, scalable, and robust solution for UMI-enabled sequencing workflows. AVAILABILITY AND IMPLEMENTATION: RUMINA is implemented in Rust and distributed as open-source code and precompiled binaries. Source code and installation instructions are available at https://github.com/greninger-lab/rumina. Documentation associated with this manuscript is available at https://github.com/greninger-lab/rumina_paper.

High-Throughput Nucleotide Sequencing↗
PubMed2026

ssHiCstuff: a package for the design and analysis of ssDNA-specific Hi-C experiments.

MOTIVATION: Single-strand DNA-specific Hi-C (ssHi-C) is a recently developed technique enabling the capture of chromatin interactions involving single-stranded DNA (ssDNA), an intermediate of various DNA metabolic processes. ssHi-C entails the restoration of restriction sites in ssDNA regions of interest upon introduction of designer, internally barcoded "annealing oligonucleotides" prior to the restriction digestion step of Hi-C. The design of these "annealing oligonucleotides," as well as the analysis of the resulting ssHi-C data presents specific challenges, such as (i) differentiating ssDNA from dsDNA-derived contacts, (ii) tracking probe-specific interactions, and (iii) calibrating the amount of ssDNA contacts across biological samples. Dedicated computational tools are therefore needed to facilitate the design of, and extract biological information from, ssHi-C experiments. RESULTS: We present ssHiCstuff, a Rust- and Python-based package for the design of key reagents for ssHi-C experiments and for the analysis of ssHi-C data. ssHiCstuff provides (i) an automated annealing oligonucleotides design module, (ii) an end-to-end analyses pipeline, and (iii) a graphical user interface. ssHiCstuff simplifies the high-resolution analysis of ssDNA interactions at genome-wide scale. A graphical user interface (GUI) implemented in Python is also available for biologists without coding skills. AVAILABILITY: ssHiCstuff is freely available at https://github.com/Piazzalab/ssHiCstuff and https://zenodo.org/records/19677479 (https://doi.org/10.5281/zenodo.19677479) under the GPL 3.0 license. The annealing oligonucleotides design and the visualization modules are additionally freely available on a web browser at https://bioshiny.ens-lyon.fr/public/app/sshicstuff. A test dataset is available at https://zenodo.org/records/20035366 (https://doi.org/10.5281/zenodo.20035366).

DNA, Single-Stranded↗
PubMed2025

Genomically integrated cassettes swapping: bringing modularity to the strain level in Saccharomyces cerevisiae.

A large variety of synthetic biology toolkits for the introduction of multiple expression cassettes is available for Saccharomyces cerevisiae. Unfortunately, none of these tools is designed to allow the modification - exchange or removal - of the cassettes already integrated into the genome in a standardized way. The application of the modularity principle therefore ends to the steps preceding the final host engineering, making microbial cell factories construction stiff and strictly sequential. In this work, we describe a system that easily allows CRISPR-mediated swapping or removal of previously integrated cassettes, thus bringing the modularity to the strain level, enhancing the possibility of modifying existing strains with a reduced number of steps. In the system, each cassette is tagged with specific barcodes, which can be used as targets for CRISPR nucleases (Cas9 and Cas12a), allowing the excision of the construct from the genome and its substitution with another expression cassette or the restoration of the wild type locus in one single standardized step. The system has been applied to the previously developed Easy-MISE toolkit and tested by swapping fluorescent protein expression cassettes with an efficiency of ∼90% quantified by PCR and flow cytometry.

Saccharomyces cerevisiae↗
PubMed2026

MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics.

Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.

Bioinformatics software↗
PubMed2025

Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.

Core promoters are essential regulatory elements that control transcription initiation, but accurately predicting and designing their strength remains challenging due to complex sequence-function relationships and the limited generalizability of existing AI-based approaches. To address this, we developed a modular platform integrating rational library design, predictive modelling, and generative optimization into a closed-loop workflow for end-to-end core promoter engineering. Conserved and spacer region of core promoters exert distinct effects on transcriptional strength, with the former driving large-scale variation and the latter enabling finer gradation. Based on this insight, Mutation-Barcoding-Reverse Sequencing approach was used and constructed a synthetic promoter library comprising 112 955 variants with minimal redundancy and a 16 226-fold expression range. A Transformer-based model trained on this dataset achieved a Pearson correlation of 0.87 with experimentally measured promoter strengths. When combined with a conditional diffusion model, the system enabled de novo generation of promoter sequences with defined strengths, achieving a design-to-measurement correlation of 0.95 and maintaining high accuracy (R = 0.93) across varied sequence contexts. The designed promoters consistently preserved their intended strength gradients, demonstrating robust plug-and-play functionality. This work establishes a scalable and extensible platform (www.yudenglab.com) for deep learning-guided programmable design of Escherichia coli core promoters, enabling precise transcriptional control.

Promoter Regions, Genetic↗