Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Predicting Splicing from Primary Sequence with Deep Learning.

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.

Mapping the Genetic Landscape of Human Cells.

Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

SCITO-seq: single-cell combinatorial indexed cytometry sequencing.

The development of DNA-barcoded antibodies to tag cell surface molecules has enabled the use of droplet-based single-cell sequencing (dsc-seq) to profile protein abundances from thousands of cells simultaneously. As compared to flow and mass cytometry, the high per cell cost of current dsc-seq-based workflows precludes their use in clinical applications and large-scale pooled screens. Here, we introduce SCITO-seq, a workflow that uses splint oligonucleotides (oligos) to enable combinatorially indexed dsc-seq of DNA-barcoded antibodies from over 105 cells per reaction using commercial microfluidics. By encoding sample barcodes into splint oligos, we demonstrate that multiplexed SCITO-seq produces reproducible estimates of cellular composition and surface protein expression comparable to those from mass cytometry. We further demonstrate two modified splint oligo designs that extend SCITO-seq to achieve compatibility with commercial DNA-barcoded antibodies and simultaneous expression profiling of the transcriptome and surface proteins from the same cell. These results demonstrate SCITO-seq as a flexible and ultra-high-throughput platform for sequencing-based single-cell protein and multimodal profiling.

Macrolide and Nonmacrolide Resistance with Mass Azithromycin Distribution.

BACKGROUND: Mass distribution of azithromycin to preschool children twice yearly for 2 years has been shown to reduce childhood mortality in sub-Saharan Africa but at the cost of amplifying macrolide resistance. The effects on the gut resistome, a reservoir of antimicrobial resistance genes in the body, of twice-yearly administration of azithromycin for a longer period are unclear. METHODS: We investigated the gut resistome of children after they received twice-yearly distributions of azithromycin for 4 years. In the Niger site of the MORDOR trial, we enrolled 30 villages in a concurrent trial in which they were randomly assigned to receive mass distribution of either azithromycin or placebo, offered to all children 1 to 59 months of age every 6 months for 4 years. Rectal swabs were collected at baseline, 36 months, and 48 months for analysis of the participants' gut resistome. The primary outcome was the ratio of macrolide-resistance determinants in the azithromycin group to those in the placebo group at 48 months. RESULTS: Over the entire 48-month period, the mean (±SD) coverage was 86.6±12% in the villages that received placebo and 83.2±16.4% in the villages that received azithromycin. A total of 3232 samples were collected during the entire trial period; of the samples obtained at the 48-month monitoring visit, 546 samples from 15 villages that received placebo and 504 from 14 villages that received azithromycin were analyzed. Determinants of macrolide resistance were higher in the azithromycin group than in the placebo group: 7.4 times as high (95% confidence interval [CI], 4.0 to 16.7) at 36 months and 7.5 times as high (95% CI, 3.8 to 23.1) at 48 months. Continued mass azithromycin distributions also selected for determinants of nonmacrolide resistance, including resistance to beta-lactam antibiotics, an antibiotic class prescribed frequently in this region of Africa. CONCLUSIONS: Among villages assigned to receive mass distributions of azithromycin or placebo twice yearly for 4 years, antibiotic resistance was more common in the villages that received azithromycin than in those that received placebo. This trial showed that mass azithromycin distributions may propagate antibiotic resistance. (Funded by the Bill and Melinda Gates Foundation and others; ClinicalTrials.gov number, NCT02047981.).

ZipSeq: barcoding for real-time mapping of single cell transcriptomes.

Spatial transcriptomics seeks to integrate single cell transcriptomic data within the three-dimensional space of multicellular biology. Current methods to correlate a cell's position with its transcriptome in living tissues have various limitations. We developed an approach, called 'ZipSeq', that uses patterned illumination and photocaged oligonucleotides to serially print barcodes ('zipcodes') onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in vitro wound healing, live lymph node sections and a live tumor microenvironment. In all cases, we discovered new gene expression patterns associated with histological structures. In the tumor microenvironment, this demonstrated a trajectory of myeloid and T cell differentiation from the periphery inward. A combinatorial variation of ZipSeq efficiently scales in the number of regions defined, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

Systematic genetic analysis of virulence in the human fungal pathogen Cryptococcus neoformans.

The fungus Cryptococcus neoformans is a leading cause of mortality and morbidity among HIV-infected individuals. We utilized the completed genome sequence and optimized methods for homologous DNA replacement using high-velocity particle bombardment to engineer 1201 gene knockout mutants. We screened this resource in vivo for proliferation in murine lung tissue and in vitro for three well-recognized virulence attributes-polysaccharide capsule formation, melanization, and growth at body temperature. We identified dozens of previously uncharacterized genes that affect these known attributes as well as 40 infectivity mutants without obvious defects in these traits. The latter mutants affect predicted regulatory factors, secreted proteins, and immune-related factors, and represent powerful tools for elucidating novel virulence mechanisms. In particular, we describe a GATA family transcription factor that inhibits phagocytosis by murine macrophages independently of the capsule, indicating a previously unknown mechanism of innate immune modulation.

Estimation of Secondary Household Attack Rates for Emergent Spike L452R Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants Detected by Genomic Surveillance at a Community-Based Testing Site in San Francisco.

BACKGROUND: Sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome from patient samples is an important epidemiological tool for monitoring and responding to the pandemic, including the emergence of new mutations in specific communities. METHODS: SARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during 22 November to 1 December, 2020, and 10-29 January 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants. RESULTS: A total of 12 124 tests were performed yielding 1099 positives. From these, 928 high-quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.4% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to the "California" or "West Coast" variants (B.1.427 and B.1.429) were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.36 vs 0.29, risk ratio [RR] = 1.28; 95% confidence interval [CI]: 1.00-1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.3%). CONCLUSIONS: The increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants. Summary: We observed a growing prevalence and modestly elevated attack rate for "West Coast" severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in a community testing setting in San Francisco during January 2021, suggesting its modestly higher transmissibility.

MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices.

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

Deep mutational scanning reveals the structural basis for α-synuclein activity.

Defining the biologically active structures of proteins in their cellular environments remains challenging for proteins with multiple conformations and functions, where only a minor conformer might be associated with a given function. Here, we use deep mutational scanning to probe the structure and dynamics of α-synuclein, a protein known to adopt disordered, helical and amyloid conformations. We examined the effects of 2,600 single-residue substitutions on the ability of intracellularly expressed α-synuclein to slow the growth of yeast. Computational analysis of the data showed that the conformation responsible for this phenotype is a long, uninterrupted, amphiphilic helix with increasing dynamics toward the C terminus. Deep mutational scanning can therefore determine biologically active conformations in cellular environments, even for a highly dynamic multi-conformational protein.

FLASH: a next-generation CRISPR diagnostic for multiplexed detection of antimicrobial resistance sequences.

The growing prevalence of deadly microbes with resistance to previously life-saving drug therapies is a dire threat to human health. Detection of low abundance pathogen sequences remains a challenge for metagenomic Next Generation Sequencing (NGS). We introduce FLASH (Finding Low Abundance Sequences by Hybridization), a next-generation CRISPR/Cas9 diagnostic method that takes advantage of the efficiency, specificity and flexibility of Cas9 to enrich for a programmed set of sequences. FLASH-NGS achieves up to 5 orders of magnitude of enrichment and sub-attomolar gene detection with minimal background. We provide an open-source software tool (FLASHit) for guide RNA design. Here we applied it to detection of antimicrobial resistance genes in respiratory fluid and dried blood spots, but FLASH-NGS is applicable to all areas that rely on multiplex PCR.

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86-1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63-0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75-1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.

Pulmonary Metagenomic Sequencing Suggests Missed Infections in Immunocompromised Children.

BACKGROUND: Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. Therefore, we aimed to develop a highly sensitive metagenomic next-generation sequencing (mNGS) assay capable of evaluating the pulmonary microbiome and identifying diverse pathogens in the lungs of immunocompromised children. METHODS: We collected 41 lower respiratory specimens from 34 immunocompromised children undergoing evaluation for pulmonary disease at 3 children's hospitals from 2014-2016. Samples underwent mechanical homogenization, parallel RNA/DNA extraction, and metagenomic sequencing. Sequencing reads were aligned to the National Center for Biotechnology Information nucleotide reference database to determine taxonomic identities. Statistical outliers were determined based on abundance within each sample and relative to other samples in the cohort. RESULTS: We identified a rich cross-domain pulmonary microbiome that contained bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Samples with bacterial outliers had significantly depressed alpha-diversity (median, 0.61; interquartile range [IQR], 0.33-0.72 vs median, 0.96; IQR, 0.94-0.96; P < .001). Potential pathogens were detected in half of samples previously negative by clinical diagnostics, demonstrating increased sensitivity for missed pulmonary pathogens (P < .001). CONCLUSIONS: An optimized mNGS assay for pulmonary microbes demonstrates significant inoculation of the lower airways of immunocompromised children with diverse bacteria, fungi, and viruses. Potential pathogens can be identified based on absolute and relative abundance. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease.

Subclonal mutations in SETBP1 confer a poor prognosis in juvenile myelomonocytic leukemia.

Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative neoplasm of childhood associated with a poor prognosis. Recently, massively parallel sequencing has identified recurrent mutations in the SKI domain of SETBP1 in a variety of myeloid disorders. These lesions were detected in nearly 10% of patients with JMML and have been characterized as secondary events. We hypothesized that rare subclones with SETBP1 mutations are present at diagnosis in a large portion of patients who relapse, but are below the limits of detection for conventional deep sequencing platforms. Using droplet digital polymerase chain reaction, we identified SETBP1 mutations in 17/56 (30%) of patients who were treated in the Children's Oncology Group sponsored clinical trial, AAML0122. Five-year event-free survival in patients with SETBP1 mutations was 18% ± 9% compared with 51% ± 8% for those without mutations (P = .006).

Compact CRISPR genetic screens enabled by improved guide RNA library cloning.

CRISPR genome editing approaches theoretically enable researchers to define the function of each human gene in specific cell types, but challenges remain to efficiently perform genetic perturbations in relevant models. In this work, we develop a library cloning protocol that increases sgRNA uniformity and greatly reduces bias in existing genome-wide libraries. We demonstrate that our libraries can achieve equivalent or better statistical power compared to previously reported screens using an order of magnitude fewer cells. This improved cloning protocol enables genome-scale CRISPR screens in technically challenging cell models and screen formats.

Immune Modulation with RANKL Blockade through Denosumab Treatment in Patients with Cancer.

Denosumab is a fully human mAb that binds receptor activator of NFκB ligand (RANKL). It is routinely administered to patients with cancer to reduce the incidence of new bone metastasis. RANK-RANKL interactions regulate bone turnover by controlling osteoclast recruitment, development, and activity. However, these interactions also can regulate immune cells including dendritic cells and medullary thymic epithelial cells. Inhibition of the latter results in reduced thymic negative selection of T cells and could enhance the generation of tumor-specific T cells. We examined whether administering denosumab could modify modulate circulating immune cells in patients with cancer. Blood was collected from 23 patients with prostate cancer and 3 patients with renal cell carcinoma, all of whom had advanced disease and were receiving denosumab, prior to and during denosumab treatment. Using high-dimensional mass cytometry, we found that denosumab treatment by itself induced modest effects on circulating immune cell frequency and activation. We also found minimal changes in the circulating T-cell repertoire and the frequency of new thymic emigrants with denosumab treatment. However, when we stratified patients by whether they were receiving chemotherapy and/or steroids, patients receiving these concomitant treatments showed significantly greater immune modulation, including an increase in the frequency of natural killer cells early and classical monocytes later. We also saw broad induction of CTLA-4 and TIM3 expression in circulating lymphocytes and some monocyte populations. These findings suggest that denosumab treatment by itself has modest immunomodulatory effects, but when combined with conventional cancer treatments, can lead to the induction of immunologic checkpoints. See related Spotlight by Nasrollahi and Davar, p. 383.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response.

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene-regulatory networks from single-cell mRNA-seq data sets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

Mycobacterium tuberculosis CYP125A1, a steroid C27 monooxygenase that detoxifies intracellularly generated cholest-4-en-3-one.

The infectivity and persistence of Mycobacterium tuberculosis requires the utilization of host cell cholesterol. We have examined the specific role of cytochrome P450 CYP125A1 in the cholesterol degradation pathway using genetic, biochemical and high-resolution mass spectrometric approaches. The analysis of lipid profiles from cells grown on cholesterol revealed that CYP125A1 is required to incorporate the cholesterol side-chain carbon atoms into cellular lipids, as evidenced by an increase in the mass of the methyl-branched phthiocerol dimycocerosates. We observed that cholesterol-exposed cells lacking CYP125A1 accumulate cholest-4-en-3-one, suggesting that this is a physiological substrate for this enzyme. Reconstitution of enzymatic activity with spinach ferredoxin and ferredoxin reductase revealed that recombinant CYP125A1 indeed binds both cholest-4-en-3-one and cholesterol, efficiently hydroxylates both of them at C-27, and then further oxidizes 27-hydroxycholest-4-en-3-one to cholest-4-en-3-one-27-oic acid. We determined the X-ray structure of cholest-4-en-3-one-bound CYP125A1 at a resolution of 1.58 A. CYP125A1 is essential for growth of CDC1551 in media containing cholesterol or cholest-4-en-3-one. In its absence, the latter compound is toxic for both CDC1551 and H37Rv when added with glycerol as a second carbon source. CYP125A1 is a key enzyme in cholesterol metabolism and plays a crucial role in circumventing the deleterious effect of cholest-4-en-3-one.