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#multiomics

3 posts3 participants1 post today

A new Collection on #cancer is out @PLOSBiology! We delve into how #omics technologies are helping researchers understand the #tumor #microenvironment (TME)

Edited by Yibin Kang, who wrote a great Editorial to frame the Collection, this group of Essays and Perspectives covers topics as diverse as:

- mutual plasticity of #cancer & immune cells in the #TME & why its relevance in therapy
- metabolic crosstalk between cancer and T cells, and how to leverage it to reprogram anti-tumor immune responses
- the potential of defining the 3D tumor neural niche using spatial omics & #AI technologies to identify new targeted therapy opportunities
- evolutionary selection operating across levels in cancer, highlighting how this understanding can be used to improve prevention and treatment
- mathematical models as tools for exploring the complex interplay between #cancer cells and their environment, and the role of AI in leading the way towards the next wave of tumor models
- how #MultiOmics data be leveraged to help cancer patients
- how single-cell technologies can help understand cancer metastatic niches and inform novel therapies
- a call for better preclinical models and improved understanding of stromal cell function in the TME

Senior cancer editor @PLOSBiology, Richard Hodge, discusses his vision for cancer biology at the journal in an accompanying Biologue post.

Take a look at let us know what you think!

plos.io/4mgHDEw

DATE: June 24, 2025 at 08:00AM
SOURCE: BioWorld MedTech

Direct article link at end of text block below.

.@illumina buying @SomaLogic for up to $425M to boost #multiomics assets

t.co/4PPqjLsW5g

#medtech

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#medtech

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t.coIllumina buying Somalogic for up to $425M to boost multiomics assetsBy Holland Johnson

DATE: June 23, 2025 at 08:00PM
SOURCE: BioWorld MedTech

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.@illumina buying @SomaLogic for up to $425M to boost #multiomics assets

t.co/4PPqjLsW5g

#medtech

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#medtech

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t.coIllumina buying Somalogic for up to $425M to boost multiomics assetsBy Holland Johnson

DATE: November 07, 2024 at 08:30AM
SOURCE: BioWorld MedTech

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Prognomiq raises $34M for early lung cancer detection test

t.co/eSaZt4griS

#medtech #PrognomiQInc @PrognomiQ_Inc #LungCancer #Multiomics

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t.co​Prognomiq raises $34M for early lung cancer detection testBy Annette Boyle

DATE: November 06, 2024 at 06:30PM
SOURCE: BioWorld MedTech

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Prognomiq raises $34M for early lung cancer detection test

t.co/eSaZt4gZ8q

#medtech #PrognomiQInc @PrognomiQ_Inc #LungCancer #Multiomics

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t.co​Prognomiq raises $34M for early lung cancer detection testBy Annette Boyle

While github repository is on private, we published a study where we identify , generated as a result of the metabolic rewiring during osteoclast differentiation. Kieler et al. Have a look! shorturl.at/M64FI

Annals of the Rheumatic Diseases · Itaconate is a metabolic regulator of bone formation in homeostasis and arthritisObjectives Bone remodelling is a highly dynamic process dependent on the precise coordination of osteoblasts and haematopoietic-cell derived osteoclasts. Changes in core metabolic pathways during osteoclastogenesis, however, are largely unexplored and it is unknown whether and how these processes are involved in bone homeostasis. Methods We metabolically and transcriptionally profiled cells during osteoclast and osteoblast generation. Individual gene expression was characterised by quantitative PCR and western blot. Osteoblast function was assessed by Alizarin red staining. immunoresponsive gene 1 ( Irg1 ) - deficient mice were used in various inflammatory or non-inflammatory models of bone loss. Tissue gene expression was analysed by RNA in situ hybridisation. Results We show that during differentiation preosteoclasts rearrange their tricarboxylic acid cycle, a process crucially depending on both glucose and glutamine. This rearrangement is characterised by the induction of Irg1 and production of itaconate, which accumulates intracellularly and extracellularly. While the IRG1–itaconate axis is dispensable for osteoclast generation in vitro and in vivo, we demonstrate that itaconate stimulates osteoblasts by accelerating osteogenic differentiation in both human and murine cells. This enhanced osteogenic differentiation is accompanied by reduced proliferation and altered metabolism. Additionally, supplementation of itaconate increases bone formation by boosting osteoblast activity in mice. Conversely, Irg1- deficient mice exhibit decreased bone mass and have reduced osteoproliferative lesions in experimental arthritis. Conclusion In summary, we identify itaconate, generated as a result of the metabolic rewiring during osteoclast differentiation, as a previously unrecognised regulator of osteoblasts. Data are available in a public, open access repository. Data are available upon reasonable request. Not applicable.

DATE: August 19, 2024 at 09:00AM
SOURCE: BioWorld MedTech

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Illumina, Grail refocus on core businesses post-split

t.co/5ih4m2HhSu

#medtech @GrailBio @Illumina #Grail #Illumina #MCED #NGS #Multiomics

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www.bioworld.comIllumina, Grail refocus on core businesses post-splitBy Annette Boyle

DATE: August 16, 2024 at 06:00PM
SOURCE: BioWorld MedTech

Direct article link at end of text block below.

Illumina, Grail refocus on core businesses post-split

t.co/5ih4m2GK2W

#medtech @GrailBio @Illumina #Grail #Illumina #MCED #NGS #Multiomics

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www.bioworld.comIllumina, Grail refocus on core businesses post-splitBy Annette Boyle

We have recently released a comprehensive Bioconductor R package, nipalsMCIA, that implements Multiple Co-Inertia Analysis (MCIA) for multi-modal (including #multiomics) data in a highly efficient manner using the NIPALS algorithm. You can read all about it in our new preprint, and/or try it out yourself! We have several comprehensive tutorials to help get you started.

Paper: biorxiv.org/content/10.1101/20

Package: bioconductor.org/packages/rele

bioRxiv · nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least SquaresMotivation: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. Results: We introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. Availability: nipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online. ### Competing Interest Statement SHK receives consulting fees from Peraton.