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How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as important regulators of cellular response to decitabine.

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that we do not understand and have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, which is a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as critical regulators of decitabine killing. Our results provide insights into the mechanisms of decitabine anti-cancer activity in treatment of AML and identify combination therapies which could potentiate decitabine anti-cancer activity.Competing Interest StatementA.Y.G. and A.A. declare no competing interests. L.A.G has filed patents on CRISPR functional genomics, has worked with the UCSF/UC Berkeley/GSK Laboratory for Genomics Research on DNMT1 inhibitors and is a co-founder of Chroma Medicine.

Synergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling—selecting representative methods based on their usage and our expertise and resources to prepare libraries—including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples. Seven methods for single-cell RNA sequencing are benchmarked on cell lines, primary cells and mouse cortex.

Black carbon (BC) particles in Beijing summer haze play an important role in the regional radiation balance and related environmental processes. Understanding the factors that lead to variability of the impacts of BC remains limited. Here, we present observations by a soot-particle aerosol mass spectrometer (SP-AMS) of BC-containing submicron particulate matter (BC−PM1) in Beijing, China, during summer 2017. These observations were compared to concurrently measured total non-refractory submicron particulate matter (NR−PM1) by a high-resolution aerosol mass spectrometer (HR-AMS). Distinct properties were observed between NR−PM1 and BC−PM1 relevant to organic aerosol (OA) composition. Hydrocarbon-like OA (HOA) in BC−PM1 was found to be up to 2-fold higher than that in NR−PM1 in fresh vehicle emissions, suggesting that a part of HOA in BC−PM1 may be overestimated, likely due to the change of collection efficiency of SP-AMS. Cooking-related OA was only identified in NR−PM1, whereas aged biomass burning OA (A-BBOA) was a unique factor only identified in BC−PM1. The A-BBOA was linked to heavily coated BC, which may lead to enhancement of the light absorption ability of BC by a factor of 2 via the “lensing effect”. More-oxidized oxygenated OA identified in BC-containing particles was found to be slightly different from that observed by HR-AMS, mainly due to the influence of A-BBOA. Overall, these findings highlight that BC in urban Beijing is partially of agricultural fire origin and that a unique biomass-burning-related OA associated with BC may be ubiquitous in aged BC−PM1, and this OA may play a role in affecting air quality and climate that has not previously been fully considered.

The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive. Current understanding of Long COVID is limited, in part, due to lack of evidence from population-representative studies. Here, the authors analyse data from ten UK population-based studies and electronic health records, and find wide variation in the frequency of Long COVID between studies but some consistent risk factors.

Proteases are frequent pharmacological targets, and their inhibitors are valuable drugs in multiple pathologies. The catalytic mechanism and the active-site fold, however, are largely conserved among the protease classes, making the development of the selective inhibitors exceedingly challenging. In our departure from the conventional strategies, we reviewed the structure of known camelid inhibitory antibodies, which block enzyme activities via their unusually long, convex-shaped paratopes. We synthesized the human Fab antibody library (over 1.25 × 10⁹ individual variants) that carried the extended, 23- to 27-residue, complementarity-determining region (CDR)–H3 segments. As a proof of principle, we used the catalytic domain of matrix metalloproteinase-14 (MMP-14), a promalignant protease and a drug target in cancer, as bait. In our screens, we identified 20 binders, of which 14 performed as potent and selective inhibitors of MMP-14 rather than as broad-specificity antagonists. Specifically, Fab 3A2 bound to MMP-14 in the vicinity of the active pocket with a high 4.8 nM affinity and was similarly efficient (9.7 nM) in inhibiting the protease cleavage activity. We suggest that the convex paratope antibody libraries described here could be readily generalized to facilitate the design of the antibody inhibitors to many additional enzymes.

Genomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13 , as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62 . We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome. It is hypothesized that there are a number of tumor specific driver genes for metastatic prostate cancer. Here, the authors perform genome-wide CRISPRi screens and integrate these data with metastatic prostate cancer functional and clinical genomics data to show that KIF4A and WDR62 drive aggressive prostate cancer phenotypes.

Chemical characterization of organic coatings is important to advance our understanding of the physio-chemical properties and environmental fate of black carbon (BC) particles. The soot-particle aerosol mass spectrometer (SP-AMS) has been utilized for this purpose in recent field studies. The laser vaporization (LV) scheme of the SP-AMS can heat BC cores gradually until they are completely vaporized, during which organic coatings can be vaporized at temperatures lower than that of the thermal vaporizer (TV) used in a standard high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) that employs flash vaporization. This work investigates the effects of vaporization schemes on fragmentation and elemental analysis of known oxygenated organic species using three SP-AMS instruments. We show that LV can reduce fragmentation of organic molecules. Substantial enhancement of C2H3O+/CO2+ and C2H4O2+ signals was observed for most of the tested species when the LV scheme was used, suggesting that the observational frameworks based on the use of HR-ToF-AMS field data may not be directly applicable for evaluating the chemical evolution of oxygenated organic aerosol (OOA) components coated on ambient BC particles. The uncertainties of H:C and O:C determined using the improved-ambient (I-A) method for both LV and TV approaches were similar, and scaling factors of 1.10 for H:C and 0.89 for O:C were determined to facilitate more direct comparisons between observations from the two vaporization schemes. Furthermore, the I-A method was updated based on the multilinear regression model for the LV scheme measurements. The updated parameters can reduce the relative errors of O:C from −26.3 % to 5.8 %, whereas the relative errors of H:C remain roughly the same. Applying the scaling factors and the updated parameters for the I-A method to ambient data, we found that even though the time series of OOA components determined using the LV and TV schemes are strongly correlated at the same location, OOA coatings were likely less oxygenated compared to those externally mixed with BC.