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The mutational mechanisms underlying recurrent deletions in clonal hematopoiesis are not entirely clear. In the current study we inspect the genomic regions around recurrent deletions in myeloid malignancies, and identify microhomology-based signatures in CALR , ASXL1 and SRSF2 loci. We demonstrate that these deletions are the result of double stand break repair by a PARP1 dependent microhomology-mediated end joining (MMEJ) pathway. Importantly, we provide evidence that these recurrent deletions originate in pre-leukemic stem cells. While DNA polymerase theta (POLQ) is considered a key component in MMEJ repair, we provide evidence that pre-leukemic MMEJ (preL-MMEJ) deletions can be generated in POLQ knockout cells. In contrast, aphidicolin (an inhibitor of replicative polymerases and replication) treatment resulted in a significant reduction in preL-MMEJ. Altogether, our data indicate an association between POLQ independent MMEJ and clonal hematopoiesis and elucidate mutational mechanisms involved in the very first steps of leukemia evolution. The mutational mechanisms that produce insertions and deletions that lead to clonal hematopoiesis are poorly understood. Here the authors show evidence that frequent deletions that are relevant to myeloid malignancies could be produced by PARP1-dependent microhomology-mediated end joining.

Acute myeloid leukemia (AML) is associated with unfavorable patient outcomes primarily related to disease relapse. Since specific types of leukemic hematopoietic stem and progenitor cells (HSPCs) are suggested to contribute to AML propagation, this study aimed to identify and explore relapse-initiating HSPC subpopulations present at diagnosis, using single-cell analysis (SCA). We developed unique high-resolution techniques capable of tracking single-HSPC-derived subclones during AML evolution. Each subclone was evaluated for chemo-resistance, in vivo leukemogenic potential, mutational profile, and the cell of origin. In BM samples of 15 AML patients, GMP-like and MLP-like HSPC subpopulations were identified as prevalent at relapse, exhibiting chemo-resistance to commonly used chemotherapy agents cytosine arabinoside (Ara-C) and daunorubicin. Reconstruction of phylogenetic lineage trees combined with genetic analysis of single HSPCs and single-HSPC-derived subclones demonstrated two distinct clusters, originating from MLP-like or GMP-like subpopulations, observed both at diagnosis and relapse. These subpopulations induced leukemia development ex vivo and in vivo. Genetic SCA showed that these relapse-related subpopulations harbored mutated EZH2 and TP53, detected already at diagnosis. This study, using combined molecular, functional, and genomic analyses at the level of single cells, identified patient-specific chemo-resistant HSPC subpopulations at the time of diagnosis, promoting AML relapse.

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development.

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics in other systems.

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Myofiber cultures give rise to myogenic as well as to non-myogenic cells. Whether these myofiber-associated non-myogenic cells develop from resident stem cells that possess mesenchymal plasticity or from other stem cells such as mesenchymal stem cells (MSCs) remain unsolved. To address this question, we applied a method for reconstructing cell lineage trees from somatic mutations to MSCs and myogenic and non-myogenic cells from individual myofibers that were cultured at clonal density.Our analyses show that (i) in addition to myogenic progenitors, myofibers also harbor non-myogenic progenitors of a distinct, yet close, lineage; (ii) myofiber-associated non-myogenic and myogenic cells share the same muscle-bound primordial stem cells of a lineage distinct from bone marrow MSCs; (iii) these muscle-bound primordial stem-cells first part to individual muscles and then differentiate into myogenic and non-myogenic stem cells.

Abstract Deep targeted sequencing technologies are still not widely used in clinical practice due to the complexity of the methods and their cost. The Molecular Inversion Probes (MIP) technology is cost effective and scalable in the number of targets, however, suffers from low overall performance especially in GC rich regions. In order to improve the MIP performance, we sequenced a large cohort of healthy individuals (n = 4417), with a panel of 616 MIPs, at high depth in duplicates. To improve the previous state-of-the-art statistical model for low variant allele frequency, we selected 4635 potentially positive variants and validated them using amplicon sequencing. Using machine learning prediction tools, we significantly improved precision of 10–56.25% (P < 0.0004) to detect variants with VAF > 0.005. We further developed biochemically modified MIP protocol and improved its turn-around-time to ∼4 h. Our new biochemistry significantly improved uniformity, GC-Rich regions coverage, and enabled 95% on target reads in a large MIP panel of 8349 genomic targets. Overall, we demonstrate an enhancement of the MIP targeted sequencing approach in both detection of low frequency variants and in other key parameters, paving its way to become an ultrafast cost-effective research and clinical diagnostic tool.

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability, and distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Instead of examining the whole genome of all cells of an organism, Microsatellite (MSs) loci which are repetitive DNA sequences of 1-6 base pairs can be used. Slippage mutations, in which repeated units are inserted or deleted, occur at relatively high rates, and thus provide high variation. These mutations are phenotypically neutral and they are highly abundant in the genome. In the past few years we developed in our lab a high throughput platform for sequencing thousands of MSs in human single cells in order to reconstruct its cell lineage tree. The method possess great computational challenges in many aspects, where one of them is assessing the ability to reconstruct lineage trees. Phylogenetic lineage tree reconstruction of cells is similar to that of organisms and species, however it has several unique characteristics that require an assessment of the accuracy of known reconstruction algorithms and developing new approaches for this application. Moreover, the increment of our ability to sequence high amount of MSs poses a new challenge of dealing with high percentage of allele dropout (DO), a major problem in this field. Therefore, various lineage reconstruction algorithms were examined using a large set of measures to test the clustering level of several cell populations within the reconstructed trees, and the distance between the reconstructed trees and the known reference trees. The evaluation was based on experimental data of somatic mutations in MSs of single cells in human and includes both in-vivo data, taken from melanoma patients, and ex-vivo data, taken from cells which were grown under a microscope with a known tree structure. In addition, extensive simulated tree data was generated including simulated MSs mutations and dropout. We found that reconstruction followed after filling the incomplete data, improves the accuracy dramatically, and that the super-tree method, Triple MaxCut (TMC), gives the highest precision tree.

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics in other systems.