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Background: In vitro studies using nucleus pulposus (NP) cells are commonly used to investigate disc cell biology and pathogenesis, or to aid in the development of new therapies. However, lab-to-lab variability jeopardizes the much-needed progress in the field. Here, an international group of spine scientists collaborated to standardize extraction and expansion techniques for NP cells to reduce variability, improve comparability between labs and improve utilization of funding and resources.
Methods: The most commonly applied methods for NP cell extraction, expansion, and re-differentiation were identified using a questionnaire to research groups worldwide. NP cell extraction methods from rat, rabbit, pig, dog, cow, and human NP tissue were experimentally assessed. Expansion and re-differentiation media and techniques were also investigated.
Results: Recommended protocols are provided for extraction, expansion, and re-differentiation of NP cells from common species utilized for NP cell culture.
Conclusions: This international, multilab and multispecies study identified cell extraction methods for greater cell yield and fewer gene expression changes by applying species-specific pronase usage, 60-100 U/ml collagenase for shorter durations. Recommendations for NP cell expansion, passage number, and many factors driving successful cell culture in different species are also addressed to support harmonization, rigor, and cross-lab comparisons on NP cells worldwide.
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http://dx.doi.org/10.1002/jsp2.1238 | DOI Listing |
PLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Systems Biology and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America.
Gene signatures predictive of chemotherapeutic response have the potential to extend the reach of precision medicine by allowing oncologists to optimize treatment for individuals. Most published predictive signatures are only capable of predicting response for individual drugs, but most chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive signatures to rank the relative predicted sensitivity of different drugs for individual tumors.
View Article and Find Full Text PDFMol Omics
September 2025
Laboratory of Structural Bioinformatics and Computational Biology, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre 91501-970, RS, Brazil.
The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
The rapid advancement of single-cell sequencing technology has generated vast amounts of multi-omics data, presenting unprecedented opportunities for single-cell multi-omics clustering analysis. However, existing single-cell clustering algorithms focus on extracting shared representations, overlooking the interactions and correlations among cells. This oversight inevitably leads to biased or confounded cell clustering results.
View Article and Find Full Text PDFSud Med Ekspert
September 2025
Bureau of Forensic Medical Expertise, Saint Petersburg, Russia.
Objective: To establish organ affiliation of liver microparticles using forensic cytological method based on hepatocytes' morphological characteristics and to determine their species belonging according to the human IgG using a quantitative enzyme-linked immunosorbent assay (ELISA).
Material And Methods: Previously dried microparticles (from 0.2×0.