Project description:Apoptosis is a critical outcome of stress-induced processes, with the endoplasmic reticulum (ER) playing a central role in apoptotic protein processing and stress signal transduction. Profiling the ER proteome during stress to cell death offers valuable insights into these processes, but existing methods often suffer from a loss of in situ information or requirement of genetic manipulation. In this study, we introduce CAT-ER, a novel non-genetic ER proteomics system that provides in situ labeling, spatiotemporal resolution, and compatibility across diverse cell types. By combining an ER-targeted iridium photocatalyst with a thio-quinone methide (thioQM) probe, CAT-ER achieves high specificity in enriching ER proteins, comparable to traditional enzymatic methods. Importantly, CAT-ER is free of genetic manipulation, allowing its use in hard-to-transfect cell types like HeLa and immune cells (e.g., Raji, Jurkat, and RAW264.7). Given the high spatiotemporal resolution of CAT-ER, we revealed dynamic ER proteome changes during thapsigargin (Tg)-induced unfolded protein response (UPR) to apoptosis. Notably, NFIP2 mitigated ER stress by halting translation when UPR initiated, while compromised EMC2 delayed apoptosis during prolonged stress. These findings provide novel insights into the molecular dynamics linking the UPR and apoptosis. Collectively, CAT-ER serves as a versatile tool for spatiotemporal proteomic analysis without the need for genetic manipulation, offering a powerful approach to study ER dynamics in various biological contexts.
Project description:Apoptosis is a critical outcome of stress-induced processes, with the endoplasmic reticulum (ER) playing a central role in apoptotic protein processing and stress signal transduction. Profiling the ER proteome during stress to cell death offers valuable insights into these processes, but existing methods often suffer from a loss of in situ information or requirement of genetic manipulation. In this study, we introduce CAT-ER, a novel non-genetic ER proteomics system that provides in situ labeling, spatiotemporal resolution, and compatibility across diverse cell types. By combining an ER-targeted iridium photocatalyst with a thio-quinone methide (thioQM) probe, CAT-ER achieves high specificity in enriching ER proteins, comparable to traditional enzymatic methods. Importantly, CAT-ER is free of genetic manipulation, allowing its use in hard-to-transfect cell types like HeLa and immune cells (e.g., Raji, Jurkat, and RAW264.7). Given the high spatiotemporal resolution of CAT-ER, we revealed dynamic ER proteome changes during thapsigargin (Tg)-induced unfolded protein response (UPR) to apoptosis. Notably, NFIP2 mitigated ER stress by halting translation when UPR initiated, while compromised EMC2 delayed apoptosis during prolonged stress. These findings provide novel insights into the molecular dynamics linking the UPR and apoptosis. Collectively, CAT-ER serves as a versatile tool for spatiotemporal proteomic analysis without the need for genetic manipulation, offering a powerful approach to study ER dynamics in various biological contexts.
Project description:Understanding cellular functions in health and disease requires dissecting spatiotemporal variations in the subcellular transcriptome. Mitochondria, with their independent RNA metabolism, play pivotal roles in numerous biological processes. Existing methods for mitochondrial RNA profiling suffer from limitations such as low resolution, contamination, and dependence on genetic manipulation. Here, we present CAT-seq, a bioorthogonal photocatalytic labeling strategy that enables high-resolution, in situ profiling of mitochondrial RNA in living cells without genetic manipulation. Through systematic exploration of quinone methide warheads, we identified an efficient RNA labeling probe. Rigorous validation and optimization enabled CAT-seq to successfully profile mitochondrial RNA, track RNA dynamics in HeLa cells, and even the challenging RAW 264.7 macrophages, achieving ~60% specificity. Furthermore, leveraging the unique reactivity of distinct quinone methides, we established an orthogonal labeling system enabling synchronous RNA and protein multi-omics profiling within the same sample. This allows us to unravel the intricate link between mitochondrial RNA and protein changes. Applying synchronous multi-omics to RAW 264.7 cells during immune response revealed an underlying mitochondrial remodeling mechanism behind oxidative phosphorylation pathway reduction. By integrating bioorthogonal photocatalytic chemistry with proximity-based labeling, CAT-seq offers a general, catalytic, and non-genetic approach for subcellular RNA and multi-omics investigations. This opens up exciting avenues for studying diverse physiological and pathological processes with RNA involvement.
Project description:Immunotherapy efficacy in solid tumors varies greatly, influenced by the tumor microenvironment (TME) and the dynamic tumor-immune interactions within it. Decoding these interactions in situ with minimal interference to native tissue architecture and delicate immune responses is critical for understanding tumor progression and optimizing therapeutic strategies. Here, we introduce CAT-Tissue, a novel deep-red photocatalytic proximity labeling method that enables ultrafast, high-resolution profiling of tumor-immune interactions in primary tissues. By leveraging nanobody-Chlorin e6 as the photocatalyst and biotin-aniline as the probe, CAT-Tissue enabled the rapid and comprehensive detection of various tumor-immune interactions in both coculture systems and primary tumor sections. Coupled with bulk RNA-sequencing, CAT-Tissue revealed distinct gene expression patterns between tumor-neighboring and tumor-distal lymphocytes, highlighting the recognition and immune responses of tumor-neighboring CD8+ T cells, which exhibited activated, effector, and exhausted phenotypes. By leveraging a deep-red photocatalytic proximity cell labeling strategy with excellent tissue penetration and biocompatibility, CAT-Tissue offers a non-genetically encoded platform with high sensitivity and spatiotemporal controllability for rapid profiling tumor-immune interactions within complex tissue environments in situ, which may advance our understanding of tumor immunology and guide the development of more effective immunotherapies.
Project description:In situ profiling of subcellular proteomic networks in primary and living systems, such as primary cells from native tissues or clinic samples, is crucial for the understanding of life processes and diseases, yet challenging for the current proximity labeling methods (e.g., BioID, APEX) due to their necessity of genetic engineering. Here we report CAT-S, a state-of-the-art bioorthogonal photocatalytic chemistry-enabled proximity labeling method, that expands proximity labeling to a wide range of primary living samples for in situ profiling of subcellular proteomes. Powered by the newly introduced thioQM labeling warhead and targeted bioorthogonal photocatalytic decaging chemistry, CAT-S enables labeling of mitochondrial proteins in living cells with high efficiency and specificity (up to 87%). We applied CAT-S to diverse cell cultures, mouse tissues as well as primary T cells from human blood, portraying the native-state mitochondrial proteomic characteristics, and unveiled a set of hidden mitochondrial proteins in human proteome. Furthermore, CAT-S allows quantitative analysis of the in situ proteomic perturbations on dysfunctional tissue samples, exampled by diabetic mouse kidneys, and revealed the alterations of lipid metabolism machinery that drive the disease progression. Given the advantages of non-genetic operation, generality, efficiency as well as spatiotemporal resolution, CAT-S may open new avenues as a proximity labeling strategy for in situ investigation of subcellular proteomic landscape of primary living samples that are otherwise inaccessible.
Project description:To investigate the effect of labeling durations on quantification bias in nucleotide conversion RNA-seq, we labeled NIH cells with 4sU. We then measured 4sU dropout in 4sU samples compared to 4sU naive samples.
Project description:We introduce Halfpipe, a tool for analyzing RNA-seq data from metabolic RNA labeling experiments. Its main features are the absolute quantification of 4sU-labeling-induced T>C conversions in the data, calculating the proportion of newly synthesized transcripts, and estimating (compartment-specific) RNA half-lives. Halfpipe excels at correcting critical biases caused by typically low labeling efficiency. We measure and compare the RNA metabolism in the G1 phase and during the mitosis of synchronized human cells. We find that RNA half-lives of constantly expressed RNAs are similar in mitosis and G1 phase, suggesting that RNA stability of those genes is constant throughout the cell cycle. Our estimates correlate well with literature values and with known RNA sequence features.