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Looking Closer to See Bigger: Challenges in Single-Cell Proteomics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 2424

Special Issue Editors


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Guest Editor
Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
Interests: LC-MS; proteomics; bottom-up proteomics; spatialOMICS; MALDI imaging; method development; single-cell analysis; biofluid proteomics; network analysis; omics analysis; thyroid diseases; cancer; lung fibrosis
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Guest Editor
Clinical Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Monza, Italy
Interests: mass spectrometry imaging; spatial omics; molecular pathology; kidney; oncology; proteomics; lipidomics; metabolomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Single-cell proteomics represents a new frontier in research opening up new possibilities in our quest to understand the complex biology behind pathological states. However, many challenges have yet to be overcome in single-cell analysis, both in terms of sample preparation methods and the management of big data. Working with single cells raises immediate concerns regarding protein quantities and concentrations. Moreover, with the advent of high-throughput and increasingly powerful technologies, single-cell analyses generate large datasets that can be difficult to manage and interpret. This Special Issue aims to untangle the challenges associated with single-cell proteomics and highlight recent advancements that have progressed the field. We invite you to contribute to this Special Issue of International Journal of Molecular Science and share your work on method development for sample preparation using both bottom-up and spatialOmics or new machine learning approaches enhancing data quality and biological interpretation.

This Special Issue is supervised by Dr. Isabella Piga and Dr. Andrew Smith, and assisted by our tap Dr. Giulia Capitoli (University of Milano-Bicocca).

Dr. Isabella Piga
Dr. Andrew Smith
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • single cell
  • proteomics
  • label-free analysis
  • mass spectrometry
  • spatial proteomics
  • mass spectrometry imaging
  • mass cytometry
  • artificial intelligence
  • clinical applications
  • deep learning
  • machine learning
  • sample preparation methods
  • laser capture microdissection
  • multiplexed proteomics
  • data-independent acquisition

Published Papers (1 paper)

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Research

23 pages, 5580 KiB  
Article
Data-Independent Acquisition Mass Spectrometry Analysis of FFPE Rectal Cancer Samples Offers In-Depth Proteomics Characterization of the Response to Neoadjuvant Chemoradiotherapy
by Aleksandra Stanojevic, Martina Samiotaki, Vasiliki Lygirou, Mladen Marinkovic, Vladimir Nikolic, Suzana Stojanovic-Rundic, Radmila Jankovic, Antonia Vlahou, George Panayotou, Remond J. A. Fijneman, Sergi Castellví-Bel, Jerome Zoidakis and Milena Cavic
Int. J. Mol. Sci. 2023, 24(20), 15412; https://doi.org/10.3390/ijms242015412 - 21 Oct 2023
Cited by 3 | Viewed by 1855
Abstract
Locally advanced rectal cancer (LARC) presents a challenge in identifying molecular markers linked to the response to neoadjuvant chemoradiotherapy (nCRT). This study aimed to utilize a sensitive proteomic method, data-independent mass spectrometry (DIA-MS), to extensively analyze the LARC proteome, seeking individuals with favorable [...] Read more.
Locally advanced rectal cancer (LARC) presents a challenge in identifying molecular markers linked to the response to neoadjuvant chemoradiotherapy (nCRT). This study aimed to utilize a sensitive proteomic method, data-independent mass spectrometry (DIA-MS), to extensively analyze the LARC proteome, seeking individuals with favorable initial responses suitable for a watch-and-wait approach. This research addresses the unmet need to understand the response to treatment, potentially guiding personalized strategies for LARC patients. Post-treatment assessment included MRI scans and proctoscopy. This research involved 97 LARC patients treated with intense chemoradiotherapy, comprising radiation and chemotherapy. Out of 97 LARC included in this study, we selected 20 samples with the most different responses to nCRT for proteome profiling (responders vs. non-responders). This proteomic approach shows extensive proteome coverage in LARC samples. The analysis identified a significant number of proteins compared to a prior study. A total of 915 proteins exhibited differential expression between the two groups, with certain signaling pathways associated with response mechanisms, while top candidates had good predictive potential. Proteins encoded by genes SMPDL3A, PCTP, LGMN, SYNJ2, NHLRC3, GLB1, and RAB43 showed high predictive potential of unfavorable treatment outcome, while RPA2, SARNP, PCBP2, SF3B2, HNRNPF, RBBP4, MAGOHB, DUT, ERG28, and BUB3 were good predictive biomarkers of favorable treatment outcome. The identified proteins and related biological processes provide promising insights that could enhance the management and care of LARC patients. Full article
(This article belongs to the Special Issue Looking Closer to See Bigger: Challenges in Single-Cell Proteomics)
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