Cycling cancer persister cells arise from lineages with distinct programs.

Non-genetic mechanisms have recently emerged as important drivers of cancer therapy failure1, where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment2. Although most cancer persisters remain arrested in the presence of the drug, a rare subset can re-enter the cell cycle under constitutive drug treatment. Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drugs. To study this rare, transiently resistant, proliferative persister population, we developed Watermelon, a high-complexity expressed barcode lentiviral library for simultaneous tracing of each cell's clonal origin and proliferative and transcriptional states. Here we show that cycling and non-cycling persisters arise from different cell lineages with distinct transcriptional and metabolic programs. Upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation are associated with persister proliferative capacity across multiple cancer types. Impeding oxidative stress or metabolic reprogramming alters the fraction of cycling persisters. In human tumours, programs associated with cycling persisters are induced in minimal residual disease in response to multiple targeted therapies. The Watermelon system enabled the identification of rare persister lineages that are preferentially poised to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

Skin-resident innate lymphoid cells converge on a pathogenic effector state.

Tissue-resident innate lymphoid cells (ILCs) help sustain barrier function and respond to local signals. ILCs are traditionally classified as ILC1, ILC2 or ILC3 on the basis of their expression of specific transcription factors and cytokines1. In the skin, disease-specific production of ILC3-associated cytokines interleukin (IL)-17 and IL-22 in response to IL-23 signalling contributes to dermal inflammation in psoriasis. However, it is not known whether this response is initiated by pre-committed ILCs or by cell-state transitions. Here we show that the induction of psoriasis in mice by IL-23 or imiquimod reconfigures a spectrum of skin ILCs, which converge on a pathogenic ILC3-like state. Tissue-resident ILCs were necessary and sufficient, in the absence of circulatory ILCs, to drive pathology. Single-cell RNA-sequencing (scRNA-seq) profiles of skin ILCs along a time course of psoriatic inflammation formed a dense transcriptional continuum-even at steady state-reflecting fluid ILC states, including a naive or quiescent-like state and an ILC2 effector state. Upon disease induction, the continuum shifted rapidly to span a mixed, ILC3-like subset also expressing cytokines characteristic of ILC2s, which we inferred as arising through multiple trajectories. We confirmed the transition potential of quiescent-like and ILC2 states using in vitro experiments, single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) and in vivo fate mapping. Our results highlight the range and flexibility of skin ILC responses, suggesting that immune activities primed in healthy tissues dynamically adapt to provocations and, left unchecked, drive pathological remodelling.

Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data.

The continued scaling of genetic perturbation technologies combined with high-dimensional assays such as cellular microscopy and RNA-sequencing has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality. Datasets emerging from these experiments can be combined to construct perturbative "maps of biology", in which readouts from various manipulations (e.g., CRISPR-Cas9 knockout, CRISPRi knockdown, compound treatment) are placed in unified, relatable embedding spaces allowing for the generation of genome-scale sets of pairwise comparisons. These maps of biology capture known biological relationships and uncover new associations which can be used for downstream discovery tasks. Construction of these maps involves many technical choices in both experimental and computational protocols, motivating the design of benchmark procedures to evaluate map quality in a systematic, unbiased manner. Here, we (1) establish a standardized terminology for the steps involved in perturbative map building, (2) introduce key classes of benchmarks to assess the quality of such maps, (3) construct 18 maps from four genome-scale datasets employing different cell types, perturbation technologies, and data readout modalities, (4) generate benchmark metrics for the constructed maps and investigate the reasons for performance variations, and (5) demonstrate utility of these maps to discover new biology by suggesting roles for two largely uncharacterized genes.

Stepwise-edited, human melanoma models reveal mutations' effect on tumor and microenvironment.

Establishing causal relationships between genetic alterations of human cancers and specific phenotypes of malignancy remains a challenge. We sequentially introduced mutations into healthy human melanocytes in up to five genes spanning six commonly disrupted melanoma pathways, forming nine genetically distinct cellular models of melanoma. We connected mutant melanocyte genotypes to malignant cell expression programs in vitro and in vivo, replicative immortality, malignancy, rapid tumor growth, pigmentation, metastasis, and histopathology. Mutations in malignant cells also affected tumor microenvironment composition and cell states. Our melanoma models shared genotype-associated expression programs with patient melanomas, and a deep learning model showed that these models partially recapitulated genotype-associated histopathological features as well. Thus, a progressive series of genome-edited human cancer models can causally connect genotypes carrying multiple mutations to phenotype.