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Camara, G. A.

Publications and source records attributed to Camara, G. A..

3 recordsLinked to original sources

Super-Resolution Visual ProteomEx for Hard Tissues and Clinical Samples

Correlating nanoscale tissue architecture with unbiased molecular composition within the same specimen remains a fundamental challenge in spatial biology. Current spatial proteomics methods either lack the imaging resolution required to resolve nanoscale tissue architecture or achieve only limited proteome coverage, and untargeted approaches capable of deep protein identification have yet to be integrated with super-resolution imaging within a single workflow. Here, we present microProteomEx, an integrated platform that combines hydrogel-assisted tissue expansion with mass spectrometry-compatible fluorescence imaging, laser capture microdissection, and bottom-up proteomics to achieve simultaneous three-dimensional super-resolution imaging (effective lateral resolution down to [~]47 nm on conventional diffraction-limited microscopes) and spatially resolved proteomics at a scalable lateral resolution of 29-100 {micro}m (0.02-0.28 nL volumetric resolution). By optimizing fixation, protein anchoring, and a secondary re-embedding strategy, we extend the method to mechanically resilient tissues, including mouse kidney and heart, as well as to formalin-fixed, paraffin-embedded clinical specimens. We demonstrate single-glomerulus and single-plaque proteomics (450-1,000 proteins per structure), resolve proteomic differences between malignant melanoma and giant congenital melanocytic nevus in a rare pediatric case, and characterize the morphology-resolved molecular heterogeneity of cored versus diffuse amyloid-beta plaques in a mouse model of Alzheimers disease across two disease stages. microProteomEx establishes a broadly accessible framework for correlating tissue ultrastructure with deep spatial proteomics, with direct implications for disease biology, biomarker discovery, and precision medicine.

biochemistry↗

Single-cell proteomics workflow for characterizing heterogeneous cell populations in saliva and tear fluid

Single-cell proteomics (SCP) has advanced considerably but still is largely limited to homogeneous populations and distant from clinical applicability. We present an SCP workflow for assessing the cellular heterogeneity in saliva and tear fluid. Initially, benchmarks were established using a standard HeLa digestion curve, resulting in more than 5,463 protein groups (PGs) at 50 pg. For single HeLa cells, the workflow was improved to minimize contamination and increase quantitative performance, reaching a maximum of 3,785 PGs per single cell. Following, SCP was benchmarked across heterogenous populations of saliva and tear fluid, collected from 10 healthy individuals. By improving cell isolation, contamination control, and DIA-based search and quantitation, single cells from saliva (n=110) and tear fluid (n=149), with average diameters of 8 and 11 {micro}m, respectively, yielded a maximum of 700 PGs per single cell. Downstream analysis indicated overrepresented protein functions, distinct cluster markers and twenty-three validated therapeutic targets identified from single-cell data. Taken together, this study demonstrates the robustness of our SCP workflow applied to biofluids, driving the discovery of biomarkers and therapeutic targets in complex microenvironments.

molecular biology↗

Saliva proteome-wide structural changes are associated with oral cancer aggressiveness

Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these potential diagnostic and prognostic factors may be regulated by events that do not imply variation in protein abundance levels, we investigated the hypothesis that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients, with and without lymph node metastasis. Fifty-one proteins with potential structural rearrangements were associated with clinical patient features. Post-translational modifications, such as glycosylation, disulfide bond, and phosphorylation, were also investigated in our data using different search engines and in silico analysis indicating that they might contribute to structural rearrangements of the potential diagnostic and prognostic markers here identified. Altogether, this powerful approach allows for a deep investigation of complex biofluids, such as saliva, advancing the search for targets for oral cancer diagnosis and prognosis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/552034v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1d0f97dorg.highwire.dtl.DTLVardef@ab9a2borg.highwire.dtl.DTLVardef@16baf1org.highwire.dtl.DTLVardef@4b4a37_HPS_FORMAT_FIGEXP M_FIG Oral cancer progression is associated with potential structural rearrangements. C_FIG

cancer biology↗