Biology subjects

Kong, A.

Publications and source records attributed to Kong, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Ageas enables time-agnostic cell fate inference from single-cell and spatial multi-omics data

Understanding cell fate decisions is fundamental to developmental biology and disease research. However, experimental lineage tracing requires genetic manipulation, which is impractical in many systems, particularly in humans. Computational approaches often rely on time-resolved measurements, which single-cell and spatial omics studies rarely provide. Here, we present Ageas, a time-agnostic transfer learning framework for cell fate inference from single-cell and spatial multi-omics data. Ageas overcomes these limitations by learning fate memory from terminal cell populations and transferring this information to progenitor or intermediate cells, enabling fate bias inference from static molecular snapshots. To enable robust generalization across molecular modalities, Ageas employs a data-adaptive ensemble strategy with automated model selection. In benchmark datasets with lineage-traced single-cell transcriptomic and epigenomic profiles, as well as spatial transcriptomics, Ageas achieves strong performance compared to existing methods. Applying Ageas to a 3D human embryo reveals a spatially organized anterior-posterior gradient of epiblast fate priming, with anterior epiblast cells biased toward ectodermal fates and posterior cells toward primitive streak-derived lineages, accompanied by regionally graded fate-associated regulatory programs. Together, these results establish Ageas as a general framework for decoding cell fate decisions from static molecular snapshots.

bioinformatics↗

bioRxiv2026

DRP1 inhibition confers cardioprotection against doxorubicin while preserving anticancer efficacy

BackgroundAnthracyclines such as doxorubicin are effective chemotherapeutics but are limited by cardiotoxicity driven in part by mitochondrial dysfunction. Dysregulated mitochondrial dynamics, particularly excessive dynamin-related protein-1 (Drp1)-mediated fission, contribute to doxorubicin-induced cardiac injury and support selective survival of cancer cells. ObjectivesTo determine whether DRP1i2, a novel small molecule Drp1 inhibitor targeting a conserved domain shared between human and mouse, can function as a cardio-oncology therapeutic by reducing doxorubicin-induced cardiotoxicity while maintaining or enhancing anti-cancer efficacy. MethodsCardioprotective effects of DRP1i2 were evaluated in a murine model of chronic doxorubicin cardiotoxicity and in human induced pluripotent stem cell-derived cardiac microtissues exposed to acute doxorubicin injury. Anticancer activity was assessed across multiple cancer cell lines using 2D monolayers and 3D microtissues. ResultsIn vivo, DRP1i2 preserved left ventricular ejection fraction, reduced interstitial fibrosis and cardiomyocyte atrophy, and attenuated doxorubicin-induced myocardial proteomic remodelling. In human cardiac microtissues, DRP1i2 improved viability and restored contractile function despite persistent mitochondrial oxidative stress. DRP1i2 showed modest anticancer activity in MG63 osteosarcoma cells in both 2D and 3D systems and did not diminish doxorubicin efficacy in other cancer models (MDA-MB-231 breast, OVCAR3 ovarian, and A549 lung adenocarcinoma). Combined treatment further enhanced cytotoxicity selectively in MG63 cells. ConclusionsDRP1i2 exerts complementary cardioprotective and anticancer actions through modulation of shared mitochondrial pathways, identifying Drp1 as a druggable target in cardio-oncology. These findings support DRP1i2 as a first-in-class Drp1 inhibitor and highlight mitochondrial dynamics as a promising therapeutic axis to preserve anthracycline efficacy while reducing cardiotoxicity. Clinical PerspectivesExcessive Drp1-mediated mitochondrial fission links anthracycline cardiotoxicity with cancer cell survival. Inhibition with DRP1i2 preserved cardiac structure and function in a chronic doxorubicin cardiotoxicity model without compromising anti-cancer activity, representing mechanism-based cardioprotection, where the heart is protected by directly targeting the molecular processes driving injury. Translation will require pharmacologic profiling and testing in tumour-bearing and comorbid models, followed by early-phase trials to confirm safety and efficacy.

pharmacology and toxicology↗

bioRxiv2025

Multi-Timescale Neural Adaptation Failure as a Mechanistic Signature of Major Depression

Major depression (MD) is usually characterized in terms of momentary abnormalities, such as negative bias or emotional blunting, derived from paradigms that measure only the first response to a stimulus. Yet depressive experience in daily life is repetitive and cumulative. Here we test the hypothesis that a failure of neural adaptation across repetitions is a core feature of MD. Using a repeated negative-stimulus paradigm, we quantified adaptation at micro (within-trial) and macro (across-session) scales via electroencephalography in 28 MD patients and 34 healthy controls, measuring both conventional event-related potentials and large-scale traveling-wave propagation. MD patients exhibited pervasive maladaptation: neural rigidity at the micro scale (absent repetition suppression) and progressive regulatory depletion at the macro scale (escalating emotional reactivity with declining cognitive control). Across analyses, adaptation-based metrics revealed larger group differences than single-shot indices, and classifiers built on adaptation features--especially traveling-wave dynamics--outperformed single-shot models (task AUC = 0.83 vs 0.78) and generalized to two independent datasets (up to AUC = 0.91). These findings challenge conventional symptom-based characterizations of MD, establishing adaptation failure--not initial reactivity--as the mechanistically primary pathological signature. This reframing provides a unified mechanistic explanation for rumination and cognitive inflexibility while offering a standardized, low-cost biomarker requiring minimal electrodes ([≤]8 channels), enabling large-scale screening in resource-limited settings.

neuroscience↗

bioRxiv2025

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

cancer biology↗

bioRxiv2025

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granulomas bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

immunology↗

bioRxiv2025

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

cancer biology↗

bioRxiv2025

QUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancer

While recent innovations in spatial biology have driven new insights into how tissue organization is altered in disease, interpreting these datasets in a generalized and scalable fashion remains a challenge. Computational workflows for discovering condition-specific differences in tissue organization typically rely on pairwise comparisons or unsupervised clustering. In many cases, these approaches are computationally expensive, lack statistical rigor, and are insensitive to low-prevalence cellular niches that are nevertheless highly discriminative and predictive of patient outcomes. Here, we present QUICHE - an automated, scalable, and statistically robust method that can be used to discover cellular niches differentially enriched in spatial regions, longitudinal samples, or clinical patient groups. In contrast to existing methods, QUICHE combines local niche detection with interpretable statistical modeling using graph neighborhoods to detect differentially enriched cellular niches, even at low prevalence. Using in silico models and spatial proteomic imaging of human tissues, we demonstrate that QUICHE can accurately detect condition-specific cellular niches occurring at a frequency of 0.5% in fewer than 20% of patient samples, outperforming the next best method which required a patient prevalence of 60% for detection. To validate our approach and understand how tumor structure influences recurrence risk in triple negative breast cancer (TNBC), we used QUICHE to comprehensively profile the tumor microenvironment in a multi-center, spatial proteomics cohort consisting of primary surgical resections, analyzing over 2 million cells from 314 patients across 5 medical centers. We discovered cellular niches that were consistently enriched in key regions of the tumor microenvironment, including the tumor-immune border and extracellular matrix remodeling regions, as well as niches statistically-associated with patient outcomes, including recurrence status and recurrence-free survival. The majority of differential niches (74.2%) were specific to patients that did not relapse and formed a robust interconnected network enriched in monocytes, macrophages, APCs, and CD8T cells with tumor and stroma cells. In contrast, the interaction network for patients that relapsed was notably sparse and enriched in B cells, CD68 macrophages and neutrophils. We validated these findings using two independent cohorts, observing similar cellular interactions and predictive power. Collectively, these results suggest that salient, generalized profiles of productive anti-tumor immune responses are defined by a network of structural engagement between innate and adaptive immunity with tumor and stromal cells, rather than by any single specific cell population. We have made QUICHE freely available as a user-friendly open-source Python package at https://github.com/jranek/quiche.

bioinformatics↗

bioRxiv2024

The effect of glucose restriction on cancer cell contractility: A threshold response in U-87 glioma

Cells rely on contractility to proliferate, and cancerous ones exhibit an increased glucose dependence. It is therefore hypothesized that glucose restriction can mitigate cancer cell proliferation by stunting their contractility. However, glucose-restriction studies have mostly been based on experiments that have yielded conflicting results; some cells become less contractile under glucose-restriction, intuitively, while, others become surprisingly more contractile. Active mechanistic modeling may prove fruitful in resolving these conflicts. In this study, we develop a model for glucose-mediated cell contractility to capture the mechanical implications of glucose restriction. The model is calibrated on cell contraction data taken from 2D-cultured glioma cells, laying on a collagen substrate. The model predicts the existence of a critical level of glucose restriction that must be exceeded for contractility to be affected, and this is validated by our experiments. Our model provides an initial step toward a fundamental understanding of the metabolic implications of cell contractility, particularly in the context of glucose restriction: an essential step in cancer studies. significanceThis study advances our understanding of how glucose restriction affects cancer cell contractility, an essential factor in cell proliferation. Our findings reveal that cells require severe glucose deprivation before exhibiting reduced contractility, highlighting a threshold response. This indicates that the cytoskeleton, a key structural component, remains active until a significant reduction in energy supply forces the cell into a lower energy state. These insights provide critical knowledge about the metabolic hierarchy within cells, contributing to the broader study of cancer metabolism and potential therapeutic strategies aimed at disrupting cellular energy pathways.

biophysics↗

bioRxiv2024

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

bioinformatics↗

bioRxiv2023

Liver-specification of human iPSC-derived endothelial cells transplanted into mouse liver

Liver sinusoidal endothelial cells (LSECs) play an important role in liver development, regeneration and pathophysiology, but the differentiation process that generates their unique tissue-specific phenotype is poorly understood and difficult to study as primary cells are only available in limited quantities. To address this, we hypothesised that human induced pluripotent stem cell (hiPSC)-derived endothelial cells (iECs) can produce hiPSC-derived LSECs upon transplantation into the livers of Fah-/-/Rag2-/-/Il2rg-/-mice, and serve as a model to study LSEC specification. Progressive and long-term repopulation of the liver vasculature was observed, as iECs expanded along the sinusoids that run between hepatocytes and increasingly produced human factor VIII, indicating differentiation into LSEC-like cells. To chart the developmental profile associated with LSEC specification, the bulk transcriptome of transplanted cells at time-points between 1 and 12 weeks were compared against primary human adult LSECs, which demonstrated a chronological increase in LSEC markers, LSEC differentiation pathways, and zonation. Bulk transcriptome analysis suggested that the transcription factors NOTCH1, GATA4, and FOS play a central role in LSEC specification, interacting with a network of 27 transcription factors. Novel markers associated with this process include EMCN and CLEC14A. Additionally, single cell transcriptomic analysis demonstrated that transplanted iECs at 4 weeks contain zonal subpopulations with a region-specific phenotype. Collectively, this study confirms that hiPSC can adopt LSEC-like features and provides insight into LSEC specification. This humanised xenograft system can be applied to further interrogate LSEC developmental biology and pathophysiology, bypassing current logistical obstacles associated with primary human LSECs.

cell biology↗

bioRxiv2022

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering

While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we developed a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

bioinformatics↗

bioRxiv2022

The Genetics of Participation: Method and Analysis

Participation in a genetic study likely has a genetic component. Identifying such component is difficult as we cannot compare genetic information of participants with non-participants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than non-participants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. A polygenic score constructed from that analysis, in non-overlapping samples, is associated with educational attainment (P = 2.1 x 10-52), body mass index (P = 1.5 x 10-19), and participation in a dietary study (P = 6.9 x 10-21). Further analysis shows that inclination to participate is a behavioural trait in its own right, and not simply a consequence of other established phenotypes. Understanding the basis of this trait is important for data analyses and the design of future surveys, genetic or otherwise.

genetics↗

bioRxiv2021

Reproducible, high-dimensional imaging in archival human tissue by Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)

Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) is a form of mass spectrometry imaging that uses metal labeled antibodies and secondary ion mass spectrometry to image dozens of proteins simultaneously in the same tissue section. Working with the National Cancer Institutes (NCI) Cancer Immune Monitoring and Analysis Centers (CIMAC), we undertook a validation study, assessing concordance across a dozen serial sections of a tissue microarray of 21 samples that were independently processed and imaged by MIBI-TOF or single-plex immunohistochemistry (IHC) over 12 days. Pixel-level features were highly concordant across all 16 targets assessed in both staining intensity (R2 = 0.94 {+/-} 0.04) and frequency (R2 = 0.95 {+/-} 0.04). Comparison to digitized, single-plex IHC on adjacent serial sections revealed similar concordance (R2 = 0.85 {+/-} 0.08) as well. Lastly, automated segmentation and clustering of eight cell populations found that cell frequencies between replicates yielded an average correlation of R2 = 0.92 {+/-} 0.06. Taken together, we demonstrate that MIBI-TOF, with well-vetted reagents and automated analysis, can generate consistent and quantitative annotations of clinically relevant cell states in archival human tissue, and more broadly, present a scalable framework for benchmarking multiplexed IHC approaches.

pathology↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗

bioRxiv2021

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand how the tumor microenvironment (TME) changes with transition to IBC, we used Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to analyze 140 clinically annotated surgical resections covering the full spectrum of breast cancer progression. We compared normal, DCIS, and IBC tissues using machine learning tools for multiplexed cell segmentation, pixel-based clustering, and object morphometrics. Transition from DCIS to IBC was found to occur along a trajectory marked by coordinated shifts in location and function of myoepithelium, fibroblasts, and infiltrating immune cells in the surrounding stroma. Taken together, this comprehensive study within the HTAN Breast PreCancer Atlas offers insight into the etiologies of DCIS, its transition to IBC, and emphasizes the importance of the TME stroma in promoting these processes.

cancer biology↗

bioRxiv2020

Family Analysis with Mendelian Imputations

Genotype-phenotype associations can be results of direct effects, genetic nurturing effects and population stratification confounding. Genotypes from parents and siblings of the proband can be used to statistically disentangle these effects. To maximize power, a comprehensive framework for utilizing various combinations of parents and siblings genotypes is introduced. Central to the approach is mendelian imputation, a method that utilizes identity by descent (IBD) information to non-linearly impute genotypes into untyped relatives using genotypes of typed individuals. Applying the method to UK Biobank probands with at least one parent or sibling genotyped, for an educational attainment (EA) polygenic score that has an R2 of 5.7% with EA, its predictive power based on direct genetic effect alone is demonstrated to be only about 1.4%. For women, the EA polygenic score has a bigger estimated direct effect on age-at-first-birth than EA itself.

genetics↗

bioRxiv2020

Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects

Associations between genotype and phenotype derive from four sources: direct genetic effects, indirect genetic effects from relatives, population stratification, and correlations with other variants affecting the phenotype through assortative mating. Genome-wide association studies (GWAS) of unrelated individuals have limited ability to distinguish the different sources of genotype-phenotype association, confusing interpretation of results and potentially leading to bias when those results are applied – in genetic prediction of traits, for example. With genetic data on families, the randomisation of genetic material during meiosis can be used to distinguish direct genetic effects from other sources of genotype-phenotype association. Genetic data on siblings is the most common form of genetic data on close relatives. We develop a method that takes advantage of identity-by-descent sharing between siblings to impute missing parental genotypes. Compared to no imputation, this increases the effective sample size for estimation of direct genetic effects and indirect parental effects by up to one third and one half respectively. We develop a related method for imputing missing parental genotypes when a parent-offspring pair is observed. We provide the imputation methods in a software package, SNIPar (single nucleotide imputation of parents), that also estimates genome-wide direct and indirect effects of SNPs. We apply this to a sample of 45,826 White British individuals in the UK Biobank who have at least one genotyped first degree relative. We estimate direct and indirect genetic effects for ∼5 million genome-wide SNPs for five traits. We estimate the correlation between direct genetic effects and effects estimated by standard GWAS to be 0.61 (S.E. 0.09) for years of education, 0.68 (S.E. 0.10) for neuroticism, 0.72 (S.E. 0.09) for smoking initiation, 0.87 (S.E. 0.04) for BMI, and 0.96 (S.E. 0.01) for height. These results suggest that GWAS based on unrelated individuals provides an inaccurate picture of direct genetic effects for certain human traits.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2017

Recurrence of de novo mutations in families

De novo mutations (DNMs) cause a large fraction of severe rare diseases of childhood. DNMs that occur in early embryos may result in mosaicism of both somatic and germ cells. Such early mutations may be transmitted to more than one offspring and cause recurrence of serious disease. We scanned 1,007 sibling pairs from 251 families and identified 885 DNMs shared by siblings (ssDNMs) at 451 genomic sites. We estimated the probability of DNM recurrence based on presence in the blood of the parent, sharing by other siblings, parent-of-origin, mutation type, and genomic position. We detected 52.1% of ssDNMs in the parental blood. The probability of a DNM being shared goes down by 2.28% per year for paternal DNMs and 1.82% for maternal DNMs. Shared paternal DNMs are more likely to be T>C mutations than maternal ones, but less likely to be C>T mutations. Depending on DNM properties, the probability of recurrence in a younger sibling ranges from 0.013% to 29.6%. We have launched an online DNM recurrence probability calculator, to use in genetic counselling in cases of rare genetic diseases.

genomics↗