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Spatially Resolved Proteome Profiling of <200 Cells from Tomato Fruit Pericarp by Integrating Laser-Capture Microdissection with Nanodroplet Sample Preparation

Due to sensitivity limitations, global proteome measurements generally require large amounts of biological starting material, which masks heterogeneity within the samples and differential protein expression among constituent cell types. Methods for spatially-resolved proteomics are being developed to resolve protein expression for distinct single cell types among highly heterogeneous tissues, but have primarily been applied to mammalian systems. Here we evaluate the performance of cell-type-specific proteome analysis of tomato fruit pericarp tissues by a platform integrating laser capture microdissection (LCM) and a recently developed automated sample preparation system (nanoPOTS, Nanodroplet processing in One pot for Trace Samples). Tomato fruits were cryosectioned prior to LCM, and tissues were dissected and captured directly into nanoPOTS chips for processing. Following processing, samples were analyzed by nanoLC-MS/MS. Approximately 1900 unique peptides and 422 proteins were identified on average from ~0.04 mm2 tissues comprising ~ 8-15 parenchyma cells. Spatially-resolved proteome analyses were performed using cells of outer epidermis, collenchyma, and parenchyma. Using =200 cells, a total of 1,870 protein groups were identified and the various tissues were easily resolved. The results provide spatial and tissue-specific insights into key enzymes and pathways involved in carbohydrate transport and source-sink relationships in tomato fruit. Of note, at the time of fruit ripening studied here, we identified differentially abundant proteins throughout the pericarp related to chlorophyll biogenesis, photosynthesis, and especially transport.

Liang, Yiran↗

HIV influences clustering and intracellular replication of hepatitis C virus

We report HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log 10 IU/mL and ~ 30%) and coinfected (7.3 log 10 IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

59 BASIC BIOLOGICAL SCIENCES↗

Spatial Proteomics towards cellular Resolution

Introduction: Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level. Areas covered: This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution. Expert opinion: The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. In conclusion, achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.

59 BASIC BIOLOGICAL SCIENCES↗