RESUMO
The expression of inhibitory immune checkpoint molecules, such as programmed death-ligand (PD-L)1, is frequently observed in human cancers and can lead to the suppression of T cell-mediated immune responses. Here, we apply expanded CRISPR-compatible (EC)CITE-seq, a technology that combines pooled CRISPR screens with single-cell mRNA and surface protein measurements, to explore the molecular networks that regulate PD-L1 expression. We also develop a computational framework, mixscape, that substantially improves the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation. Applying these tools, we identify and validate regulators of PD-L1 and leverage our multimodal data to identify both transcriptional and post-transcriptional modes of regulation. Specifically, we discover that the Kelch-like protein KEAP1 and the transcriptional activator NRF2 mediate the upregulation of PD-L1 after interferon (IFN)-γ stimulation. Our results identify a new mechanism for the regulation of immune checkpoints and present a powerful analytical framework for the analysis of multimodal single-cell perturbation screens.
Assuntos
Antígeno B7-H1/genética , Proteínas de Checkpoint Imunológico/fisiologia , Análise de Célula Única/métodos , Antígeno B7-2/metabolismo , Antígeno B7-H1/metabolismo , Proteínas de Ciclo Celular/genética , Proteínas de Ciclo Celular/metabolismo , Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Proteínas Culina/genética , Proteínas Culina/metabolismo , Humanos , Proteína 1 Associada a ECH Semelhante a Kelch/genética , Fator 2 Relacionado a NF-E2/genética , Fator 2 Relacionado a NF-E2/metabolismo , Proteína 2 Ligante de Morte Celular Programada 1/metabolismo , Receptores de Interferon/genética , Reprodutibilidade dos Testes , Razão Sinal-Ruído , Células THP-1 , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismoRESUMO
Levels of gene expression underpin organismal phenotypes1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown1,2. Here we assayed gene expression in rice (Oryza sativa)3, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic6 (0.04%), and transcripts that display lower levels of expression and stochastic noise7-9 and higher levels of plasticity9 are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity9. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes4,5, but that the efficacy of selection is genetically constrained under drought conditions10. Drought selected for earlier flowering11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering13-marking this gene as a drought-escape gene11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.
Assuntos
Regulação da Expressão Gênica de Plantas , Oryza/genética , Seleção Genética/genética , Secas , Evolução Molecular , Flores/genética , Flores/crescimento & desenvolvimento , Aptidão Genética/genética , Oryza/crescimento & desenvolvimento , Fotossíntese/genética , Folhas de Planta/genética , RNA Mensageiro/análise , RNA Mensageiro/genética , Fatores de Tempo , Fatores de Transcrição/metabolismoRESUMO
Droplet-based single-cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While this approach offers the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we developed a 3D-printed, low-cost droplet microfluidic control instrument and deploy it in a clinical environment to perform single-cell transcriptome profiling of disaggregated synovial tissue from five rheumatoid arthritis patients. We sequence 20,387 single cells revealing 13 transcriptomically distinct clusters. These encompass an unsupervised draft atlas of the autoimmune infiltrate that contribute to disease biology. Additionally, we identify previously uncharacterized fibroblast subpopulations and discern their spatial location within the synovium. We envision that this instrument will have broad utility in both research and clinical settings, enabling low-cost and routine application of microfluidic techniques.