Kathleen T. Nevola

Kathleen T. Nevola

Auto-generated profile, based on publicly available information

Kathleen T. Nevola

Olink, part of Thermo Fisher Scientific, Uppsala, 75330, Sweden

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 February 2022
Verified
Dataset
Closed access
KNKathleen Nevola
Kathleen Nevola
MSMarianne Sandin
Marianne Sandin
JGJamey Guess
Jamey Guess
SFSimon Forsberg
Simon Forsberg
CCChristoffer Cambronero
Christoffer Cambronero
KHKristian Persson Hodén
Kristian Persson Hodén

9 July 2026 in Research Square
Verified
Preprint
Open access
PDF file
DKDeepti J. Kundu
Deepti J. Kundu
ALAsier Larrea
Asier Larrea
SKSelvakumar Kamatchinathan
Selvakumar Kamatchinathan
CBChakradhar Bandla
Chakradhar Bandla
NJNithu Sara John
Nithu Sara John
YPYasset Perez-Riverol
Yasset Perez-Riverol
High-throughput affinity proteomics (AP) technologies, such as Olink and SomaScan, are central to biomarker discovery and clinical studies, but unlike mass spectrometry (MS)-based proteomics, they have lacked a dedicated, standards-driven open-data repository. To address this gap, we introduce the PRIDE Affinity Proteomics Archive (PRIDE-AP), a centralized, open, technology-agnostic repository extending the PRIDE infrastructure to support submission, storage, and reuse of AP datasets in compliance with FAIR principles. Each dataset receives a stable PAD identifier and DOI and undergoes automated validation. We extended the HUPO-PSI SDRF-Proteomics format and the PRIDE controlled vocabulary for AP experiments, and integrated quality-control statistics via the open-source library pyprideap. As of May 2026, PRIDE-AP includes 20 public datasets spanning 14 human diseases, with evidence for over 10,000 proteins. By enabling AP and MS datasets to be jointly deposited and linked, with protein-centric exploration cross-referenced to UniProt, PRIDE-AP enables cross-technology proteome interrogation at scale.

10 January 2020 in bioRxiv
Verified
Preprint
Open access
PDF file
ATAnnika Treyball
Annika Treyball
ABAudrey C. Bergeron
Audrey C. Bergeron
DBDaniel J. Brooks
Daniel J. Brooks
ALAudrie L. Langlais
Audrie L. Langlais
HHHina Hashmi
Hina Hashmi
KMKatherine J. Motyl
Katherine J. Motyl
Although the non-selective β-blocker, propranolol, improves bone density with PTH treatment in mice, the mechanism of this effect is unclear. To address this, we used a combination of in vitro and in vivo approaches to address how propranolol influences bone remodeling in the context of PTH treatment. In female C57BL/6J mice, intermittent PTH and propranolol had complementary effects in the trabecular bone of the distal femur and L5 vertebra, with combination treatment achieving micro-architectural parameters beyond that of PTH alone. Combined treatment improved the serum bone formation marker, P1NP, but did not impact other histomorphometric parameters relating to osteoblast function at the L5. In vitro , propranolol amplified the acute, PTH-induced, intracellular calcium signal in osteoblast-like cells. The most striking finding, however, was suppression of PTH-induced bone resorption. Despite this, PTH-induced receptor activator of nuclear factor kappa-B ligand (RANKL) mRNA and protein levels were unaltered by propranolol, which led us to hypothesize that propranolol could act directly on osteoclasts. Using in situ methods, we found Adrb2 expression in osteoclasts in vivo , suggesting β-blockers may directly impact osteoclasts. Taken together, this work suggests a strong anti-osteoclastic effect of non-selective β-blockers in vivo , indicating that combining propranolol with PTH could be beneficial to patients with extremely low bone density.

8 June 2026
Verified
Dataset
Closed access
KNKathleen Nevola
Kathleen Nevola
MSMarianne Sandin
Marianne Sandin
JGJamey Guess
Jamey Guess
SFSimon Forsberg
Simon Forsberg
CCChristoffer Cambronero
Christoffer Cambronero
KDKang Dong
Kang Dong
Exemplifying analysis of large-scale protein data from the 'Olink platform', primarily relative protein expression data that has been exported from 'Olink NPX Software', as well as QUANT data from 'Olink'. QUANT data is log-transformed. Materials focus on reading data, demonstrating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to guide users extract biological insights from large-scale protein data run on the 'Olink platform'. More information on 'Olink' data can be found at .