Genetic interaction networks map functional connections between genes and their corresponding pathways and complexes. We previously developed TheCellMap.org as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) analysis in the budding yeast, Saccharomyces cerevisiae. We have expanded TheCellMap.org to include ~89,000 quantitative genetic interactions identified from genome-scale CRISPR-based analysis of ~4 million human gene pairs in the haploid cell line, HAP1. TheCellMap.org enables users to readily access, visualize and explore human HAP1 genetic interactions, as well as to extract and reorganize sub-networks, applying data-driven network layouts in an intuitive and interactive manner.

Forward genetic screens are powerful tools for novel biological discoveries because they do not require a priori knowledge of genes controlling a phenotype of interest. The Sleeping Beauty (SB) transposon system is one of many commonly employed genetic screen tools used to induce both gain and loss of gene function mutations via insertional mutagenesis. In the past, SB screens have been successfully utilized to discover several cancer driver genes that could serve as potential therapeutic targets. More recently, SB technologies have been extended to other applications, including the discovery of mechanisms contributing to drug resistance and novel targets for immunotherapy. Importantly, existing tools for SB screen data analysis do not support data-driven case-control comparisons capable of analyzing screens with this design. To this end, we developed a network-based common insertion site (NetCIS) analysis tool to robustly identify common insertion sites (CISes) in a case-control phenotype selection screen. NetCIS uses an efficient graph-based algorithm for discovering statistically significant CISes that differ between cases and controls. We benchmark NetCIS against other insertional mutagenesis analysis tools using a previously published SB dataset and show that NetCIS is able to prioritize previous biologically validated genes similarly or better than competing methods, is the only method that effectively utilizes a case-control experimental setup, and can identify CISes involving unannotated regions of the genome that are overlooked by existing methods. Code for NetCIS and an accompanying tutorial can be found at https://github.com/RogersLabGroup/NetCIS.

Deciphering how genes interact within human cells is essential for understanding their functional wiring and for developing targeted therapeutic strategies. In this study, we present a genome-scale map of genetic interactions in the human haploid cell line HAP1, based on CRISPR-based perturbation of ∼4 million gene pairs. The resulting network comprises ∼89,000 high-confidence gene-gene interactions, organizing genes into hierarchical modules corresponding to protein complexes and pathways, biological processes, and cellular compartments, mirroring principles observed in yeast and highlighting the functional architecture of a human cell. This large-scale genetic network complements the DepMap gene co-essentiality network by capturing unique functional information, uncovering roles of previously uncharacterized genes, and identifying molecular determinants of cancer-cell-line-specific genetic dependencies. This study presents a general data-driven strategy for systematically exploring the roles of genes and their functional connections in human cell lines.

Abstract Drugs that slow the rate of organismal aging (geroprotectors) have the potential to improve human healthspan by preventing the development of multiple chronic diseases. The nematode C. elegans is a proven system for identifying anti-aging drugs, but methods for candidate nomination that scale to high throughput remain limited and have not been widely adopted. To accelerate the discovery process, we have developed an open-source AI-enabled screening platform that provides a rapid, automated, posture-based score of C. elegans survival. We used this workflow to screen a library of 2,782 FDA-approved drugs and identified 31 compounds that reproducibly increase heat stress resistance, a known predictor of longevity. Follow-up studies confirmed that many of these compounds confer lifespan extension in worms, and several compounds also have anti-senescent activity in human cells. This simplified and adaptable AI-enabled workflow therefore has the potential to accelerate the discovery of translatable drugs that slow aging.

We present Orobas, a computational approach for transforming raw read count data from CRISPR-Cas9 chemical-genetic screens into quantitative interaction scores. We describe steps for computing differential interaction scores with statistical tests that account for multiple CRISPR guides per gene. We then outline approaches for post-processing differential log2-fold-change scores across multiple screens, incorporating normalization to reduce technical artifacts and correct batch effects.

APOBEC3 (A3) enzymes play a pivotal role in mutagenesis across various cancer types. These enzymes, which are single-stranded DNA cytosine deaminases, convert cytosine to uracil (C-to-U) as part of innate antiviral immune responses. Aberrant expression of A3 enzymes in several cancers leads to DNA damage, mutagenesis, and genomic instability. In breast cancer, in particular, APOBEC3B (A3B) is the major endogenous mutator that contributes to tumor evolution and resistance to treatment (1–4). DNA C-to-U deamination events result in characteristic C-to-T transitions and C-to-G transversions, and genomic uracils can also be processed into single- or double-stranded DNA breaks and larger chromosomal aberrations. Consequently, tumors with elevated A3B levels endure chronic genotoxic stress and may exhibit increased sensitivity to inhibitors targeting specific DNA repair pathways. Previous research from our laboratory identified DNA uracil glycosylase 2 (UNG2), a critical initiator of the base excision repair (BER) pathway, as a synthetic lethal partner with A3B (5). Genetic disruption of UNG2, combined with high A3B expression, led to cell death (5). This finding suggests that other DNA repair proteins could potentially serve as synthetic lethal partners with A3B under conditions of elevated A3B-induced DNA damage. To test this hypothesis, we used pharmacological and genetic inhibition of DNA damage repair proteins in isogenic cell line models for A3B. Specifically, we quantified cancer cell viability following treatment with various commercially available DNA damage response inhibitors, in the presence or absence of A3B. Additionally, we performed CRISPR screens using a guide RNA library targeting 237 DNA damage repair and response genes in a doxycycline-inducible TREX-293-A3Bi-eGFP cell line. Cells expressing or lacking A3B were harvested for DNA extraction and sequencing at different time points. By comparing guide RNA abundance between doxycycline-treated cells and untreated cells, we identified dropout guides that disrupt genes, creating potential synthetic lethal combinations with A3B. The results from both pharmacological and genetic inhibition approaches will be presented. Selected References: 1. Burns, M. B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013). 2. Law, E. K. et al. The DNA cytosine deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast cancer. Sci Adv 2, e1601737 (2016). 3. Bertucci, F. et al. Genomic characterization of metastatic breast cancers. Nature 569, 560–564 (2019). 4. Venkatesan, S. et al. Induction of APOBEC3 exacerbates DNA replication stress and chromosomal instability in early breast and lung cancer evolution. Cancer Discov (2021) doi:10.1158/2159-8290.CD-20-0725. 5. Serebrenik, A. A. et al. The deaminase APOBEC3B triggers the death of cells lacking uracil DNA glycosylase. Proc Natl Acad Sci USA 116, 22158–22163 (2019). Citation Format: Bojana Stefanovska, Benjamin Troness, Kevin Lin, Chad Myers, Reuben S. Harris. APOBEC3B And DNA Damage Repair As Synthetic Lethal Pairs [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-06-26.

Acute myeloid leukemia (AML) is an aggressive blood cancer. TP53 mutations (TP53Mut) confer the worst prognosis in AML. Leukemia stem cells (LSCs) are rare AML cells endowed with self-renewal capacity, allowing them to recapitulate leukemia after therapy and cause disease progression. The pathways that mediate self-renewal in human TP53Mut AML are not well-known. To define the signaling pathways that are activated in TP53Mut human LSCs, we used CyTOF to profile primary human AML samples. TP53Mut LSCs displayed significantly elevated levels of activated (phosphorylated) NF kappa B (pNFkB/p65-S529) and pSTAT1, demonstrating a unique signaling activation state in TP53Mut AML. Next, we sought to predict how these signaling molecules interact in TP53Mut LSCs. To model the signaling protein architecture, we used a machine learning algorithm that has been validated as a method to discover statistical correlations and dependencies between signaling molecules, called Bayesian networks modeling. In TP53Mut LSCs, NFkB displayed a strong influence on the levels of phosphorylated Histone H3 and Ki67, suggesting that proliferation depends on NFkB in TP53Mut LSCs. Next, we asked whether the elevated NFkB levels in TP53Mut LSCs depend on mutant p53. Indeed, we found that knockdown of TP53 reduced NFkB protein levels in Kasumi AML cells (which are TP53R248Q/-) but not in MOLM13 AML cells (which are TP53WT). RNA sequencing showed that TP53 knockdown led to loss of NFkB and LSC self-renewal signatures in Kasumi cells. Furthermore, TP53 knockdown in two primary human TP53Mut AML samples led to a loss of NFkB and LSC transcriptional signatures in these primary samples as well. Serial colony formation in Kasumi cells and TP53Mut primary human AML samples was also abrogated after TP53Mut knockdown. These data suggest that TP53Mut LSCs depend on mutant p53-driven activation of NFkB for self-renewal. Since clinical-grade, direct NFkB inhibitors are not available, we used proteasome inhibitors to test whether NFkB inhibition can kill TP53Mut AML. We treated six primary human TP53Mut AML samples with the proteasome inhibitors, carfilzomib and bortezomib (FDA approved for lymphoid malignancies). Each drug independently reduced NFkB levels and led to a significant reduction of serial colony formation. Next, we treated TP53 knocked-down and control Kasumi cells with bortezomib. In control cells (which harbor mutant p53), bortezomib reduced stem cell markers and activated signaling molecules. However, the effect of bortezomib was significantly attenuated in TP53 knockdown cells, suggesting that the effects of bortezomib in AML are enhanced by mutant p53. Together, these data suggest that TP53Mut AML may rely on the NFkB pathway for self-renewal and that inhibiting this pathway with proteasome inhibitors may target the LSCs of this treatment-refractory AML subtype. Daniel Chang, Marie Lue Antony, Klara E. Noble-Orcutt, Yoonkyu Lee, Vidhyalakshmi Ramesh, Karen Sachs, Chad Myers, Zohar Sachs. TP53 mutant human acute myeloid leukemia stem cells rely on the NFkB pathway for self-renewal and are sensitive to proteasome inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3780.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenetic approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued proliferation inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in antagonizing G1 progression in a diversity of cell linages, including chronic myeloid leukemia (CML), breast cancer, and immortalized cell lines.

Small molecules that induce non-apoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here, we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of non-apoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very long chain fatty acids but appears to promote non-apoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent non-apoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

The regulation of metabolism is vital to any organism and can be achieved by transcriptionally activating or repressing metabolic genes1-3. Although many examples of transcriptional metabolic rewiring have been reported4, a systems-level study of how metabolism is rewired in response to metabolic perturbations is lacking in any animal. Here we apply Worm Perturb-Seq (WPS)-a high-throughput method combining whole-animal RNA-interference and RNA-sequencing5-to around 900 metabolic genes in the nematode Caenorhabditis elegans. We derive a metabolic gene regulatory network (mGRN) in which 385 perturbations are connected to 9,414 genes by more than 110,000 interactions. The mGRN has a highly modular structure in which 22 perturbation clusters connect to 44 gene expression programs. The mGRN reveals different modes of transcriptional rewiring from simple reaction and pathway compensation to rerouting and more complex network coordination. Using metabolic network modelling, we identify a design principle of transcriptional rewiring that we name the compensation-repression (CR) model. The CR model explains most transcriptional responses in metabolic genes and reveals a high level of compensation and repression in five core metabolic functions related to energy and biomass. We provide preliminary evidence that the CR model may also explain transcriptional metabolic rewiring in human cells.

Identifying genes important for fitness in Candida albicans advances our understanding of this important pathogen of humans. Here, we present a functional genomics approach for assessing fitness through the quantification of strain-specific barcodes. We describe steps for library preparation, propagation of strains, genomic DNA extraction, amplification of barcodes, and sequencing. We then detail the computational analysis of data to determine effect size and statistical significance. For complete details on the use and execution of this protocol, please refer to Xiong et al.1.

Cardiolipin (CL) is the signature phospholipid of the inner mitochondrial membrane, where it stabilizes electron transport chain protein complexes1. The final step in CL biosynthesis relates to its remodelling: the exchange of nascent acyl chains with longer, unsaturated chains1. However, the enzyme responsible for cleaving nascent CL (nCL) has remained elusive. Here, we describe ABHD18 as a candidate deacylase in the CL biosynthesis pathway. Accordingly, ABHD18 converts CL into monolysocardiolipin (MLCL) in vitro, and its inactivation in cells and mice results in a shift to nCL in serum and tissues. Notably, ABHD18 deactivation rescues the mitochondrial defects in cells and the morbidity and mortality in mice associated with Barth syndrome. This rare genetic disease is characterized by the build-up of MLCL resulting from inactivating mutations in TAFAZZIN (TAZ), which encodes the final enzyme in the CL-remodelling cascade1. We also identified a selective, covalent, small-molecule inhibitor of ABHD18 that rescues TAZ mutant phenotypes in fibroblasts from human patients and in fish embryos. This study highlights a striking example of genetic suppression of a monogenic disease revealing a canonical enzyme in the CL biosynthesis pathway.

Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screening platform by generating a DNA damage response (DDR)-focused custom sgRNA library targeting 1011 genes with 3033 sgRNAs. We performed five proof-of-principle compound screens and found that the compounds’ known modes-of-action (MoA) were enriched among the compounds’ CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale with 20-fold fewer resources than commonly used genome-wide libraries and produce biologically informative CGI profiles.

Abstract In acute myeloid leukemia (AML), TP53 mutations ( TP53Mut ) confer the worst prognosis. Leukemia stem cells (LSCs) are rare AML cells endowed with self-renewal capacity, allowing them to recapitulate leukemia after therapy and cause disease progression. In human AML, signaling pathways associated with inflammation have been implicated in the pathogenesis of TP53Mut AML, but the pathways that mediate self-renewal in human TP53Mut AML are not well-known. To define the signaling pathways that are activated in TP53Mut human LSCs, we used CyTOF, a form of flow cytometry that can measure up to 40 proteins simultaneously at single-cell resolution. We compared the levels of activated signaling molecules in the LSC-enriched (CD34+CD38−) subset of seven TP53Mut and seven TP53 wild-type ( TP53WT ) AML samples. Phosphorylated NF kappa B (pNFkB), pSTAT1, and pP38 are significantly higher in TP53Mut AMLs. In contrast, Ki67 and pMAPKAPKII are higher in TP53WT AML. These data demonstrate a unique signaling activation state in TP53Mut AML. The influence of signaling molecules on downstream targets can vary by cellular context. Therefore, we asked whether the influence and interaction of the activated signaling molecules vary between TP53Mut and TP53WT LSCs. To model the signaling architecture, we performed Bayesian networks modeling, a machine learning algorithm that has been validated as a method to discover statistical correlations and dependencies between signaling molecules. We found a similar global signaling network structure and hierarchy in TP53Mut and TP53WT AMLs. However, TP53Mut model showed NFkB have strong influences on phosphorylated Histone H3 (pHIS3) and Ki67, suggesting that proliferation depends on NFkB levels in TP53Mut LSCs. Furthermore, NFkB levels were highly dependent on pSTAT1 in TP53Mut LSCs. Next, we compared the transcriptional profiles of TP53Mut and TP53WT samples in the BEAT AML dataset and found that TP53Mut AML displayed significant enrichment of all the NFkB gene sets (n=19) in the molecular signatures database, providing more evidence for a unique reliance of TP53Mut human AML on NFkB signaling. Therefore, we used lentivirus transduced shRNA constructs to determine the influence of mutant p53 on signaling in human AML. We found that knockdown of TP53Mut reduced NFkB protein levels in Kasumi AML cells (which are TP53R248Q/ − ) but not in MOLM13 AML cells (which are TP53WT). RNA sequencing of transduced Kasumi cells showed that TP53Mut knockdown led to loss of NFkB and LSC self-renewal signatures. Knockdown of TP53Mut in primary human TP53Mut AMLs (S127F/- and S241Y/-) led to a similar loss of NFkB and LSC signatures in these primary samples as well. Colony formation in Kasumi cells and TP53Mut primary human AML were also abrogated after TP53Mut knockdown. These data show TP53Mut promotes NFkB activation in human AML and suggests that human TP53Mut LSCs may be dependent on NFkB for self-renewal. These data implicate NFkB as a possible therapeutic strategy to target TP53Mut human LSCs. Citation Format: Daniel Chang, Klara Noble-Orcutt, Marie Lue Antony, Yoonkyu Lee, Fiona He, Karen Sachs, Chad L Myers, Zohar Sachs. Bayesian networks modeling identifies a reliance of TP53 mutant AML on NF kappa B signaling [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P34.

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

Fungal pathogens such as Candida albicans pose a significant threat to human health with limited treatment options available. One strategy to expand the therapeutic target space is to identify genes important for pathogen growth in host-relevant environments. Here, we leverage a pooled functional genomic screening strategy to identify genes important for fitness of C. albicans in diverse conditions. We identify an essential gene with no known Saccharomyces cerevisiae homolog, C1_09670C, , and demonstrate that it encodes subunit 3 of replication factor A (Rfa3). Furthermore, we apply computational analyses to identify functionally coherent gene clusters and predict gene function. . Through this approach, we predict the cell-cycle-associated function of C3_06880W, , a previously uncharacterized gene required for fitness specifically at elevated temperatures, and follow-up assays confirm that C3_06880W encodes Iml3, a component of the C. albicans kinetochore with roles in virulence in vivo. . Overall, this work reveals insights into the vulnerabilities of C. albicans. .

Introduction Single cell RNA sequencing (scRNAseq) is commonly used to determine cell identity. Several scRNAseq analysis methods are available. Most methods use arbitrary scores to represent the similarity of the query cell to a reference profile and do not assess the statistical significance of those scores. We previously presented a method to identify cells that express a profile of interest (the reference profile) within a scRNAseq dataset. Our method, called Single Cell Correlation Analysis (SCA), identifies cells in a query dataset that are similar to a reference profile. SCA represents a novel advance in the field because it calculates the statistical significance of the correlation coefficient using a permutation-based false discovery rate (FDR) estimation. Here, we advanced SCA to uniformly identify cells across multiple scRNAseq datasets. The new method is called SCA-Across Datasets (SCA-AD). We tested the performance of SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow, benchmarked SCA-AD to other methods, and used SCA-AD to identify and compare self-renewing cells in human AML samples from adult and pediatric patients. Methods and Results SCA-AD uses a universal scoring system to assign cell identity across datasets. Spearman's correlation is used to score the similarity of query cells to a reference profile. SCA-AD integrates a common background dataset into each query dataset which ensures heterogeneity in the data and allows SCA-AD to establish a threshold value for cell identity across the datasets. FDR calculations are used to establish the threshold value. We tested SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow. In both cases, the self-renewing compartment was experimentally-validated using gold-standard, in vivo self-renewal assays. We extracted a reference normal bone marrow self-renewal profile from the in vivo-validated murine scRNAseq dataset (Rodriguez-Fraticelli et al. Nature 2020). We used this reference profile to query a human scRNAseq dataset of normal bone marrow precursors with experimentally-validated self-renewal capacity (Velten et al. Nature Cell Biology, 2017). We compared the performance of SCA-AD to other commonly-used methods: scmap-cluster (Kiselev et al. Nature Methods 2018), singleR (Aran et al. Nature Immunology 2019), scType (Ianevski et al. Nature Communications 2022), AUCell (Aibar et al. Nature Methods 2017). SCA-AD matched or exceeded the sensitivity and precision of each of these methods when applied to the Velten dataset. We found that, at a false discovery rate (FDR) of 0.001, SCA-AD identified self-renewing cells with 63% sensitivity and 27% precision. All but the scmap-cluster method matched SCA-AD in sensitivity and all the methods displayed a significantly lower precision (11-19%). To ensure that SCA-AD can determine if the cell of interest is absent in the data, we removed all experimentally-validated self-renewing cells from the Velten dataset. All the other methods identified 27-100% self-renewing cells in the case when the self-renewing cells were omitted from the data. In contrast, SCA-AD identified 5.9% self-renewing cells with an FDR of 0.001, demonstrating a very low false positive rate and the high specificity of SCA-AD. Finally, we applied SCA-AD to scRNAseq datasets of AML from 16 adult (van Galen et al. Cell, 2019) and 13 pediatric (Zhang et al. Genome Biology, 2023) patients. For a reference profile, we used the leukemia self-renewal profile that we defined previously and validated in vivo (Sachs et al. Cancer Research 2020). SCA-AD identified self-renewing cells in each of the 29 AML samples. In adults, the self-renewing cells were most prevalent in GMPs and progenitors. In contrast, the self-renewing cells in the pediatric samples were more prevalent among GMPs and LMPPs, suggesting differences in the stem cell compartment between pediatric and adult AML. Pathway analysis revealed enrichment of Myc and mitochondrial profiles in the self-renewing cells. Conclusions We developed SCA-AD, a novel method to uniformly identify cells across datasets with a statistical confidence measure. SCA-AD performs well in experimentally-validated datasets, can determine if a cell of interest is absent in the data, and matches or exceeds the performance of existing methods. Finally, we used SCA-AD to compare features of self-renewing cells in adult and pediatric AML.