#transcriptome

[bpod-bpod]

Growing Liver

Study of the genes being read (transcriptome) in organoids [3D lab-grown tissue] grown from human foetal liver cells (hepatocytes) reveals the molecules they require for their metabolism and to undergo cell division as they mature from development to adulthood

Read the published research article here

Image from work by Delilah Hendriks and Benedetta Artegiani, and colleagues

Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and The Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Nature Communications, May 2024

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[cytgen]

Abstract To obtain information of Periplaneta americana, we analyzed the distribution characteristics of microsatellite sequences in the P. americana transcriptome (229 MB) by using MSDBv2.4. The total number of perfect microsatellite sequences was 38 082 and covered about 0.3% of P. americana transcriptome. The cumulative length of microsatellites was 618 138 bp, and the density of microsatellites was 2978.54 bp/Mb. In the different repeat types of the microsatellites, the number of the mononucleotide repeats was 20 002 (accounting for 52.52%), which obviously was the most abundant type. While the trinucleotide, tetranucleotide, dinucleotide, pentanucleotide and hexanucleotide repeats accounted for 24.51, 12.97, 8.13, 1.61 and 0.26%, respectively. The kind of different repeat copy categories in each repeat type was also quite different, such as the A in mononucleotide repeat type, the AG in dinucleotide, the AAT in trinucleotide, AAAT in tetranucleotide, the AAGAA in pentanucleotide, and the CAGTAG in hexanucleotide were the most of each category. The A, T, AC, AG, AT, GT, AAG, AAT, ATC, ATG, ATT, CTT, AAAG and AAAT were the dominant repeat copy categories, the total number of all these types was 29 933, accounting for 78.6% in the total number of microsatellite sequences. These results based on a foundation for developing high polymorphic microsatellites to research the functional genomics, population genetic structure and genetic diversity of P. americana.

[kentaroshimizu-ycu]

トランスクリプトーム解析技術の公開

以前横市にも頻繁に来て下さっていた農研機構の孫さん、当グループの清水先生、爲重さん、岡田さんも一緒に書いた論文が出ました。コムギで多検体のトランスクリプトーム解析をするのに優れた手法開発の論文です。

[hevearesearch]

De novo Assembly and Characterization of Bark Transcriptome using Illumina Sequencing and Development of EST-SSR Markers in Rubber Tree (Hevea brasiliensis Muell. Arg.)

Dejun Li, Zhi Deng, Bi Qin, Xianghong Liu, and Zhonghua Men BMC Genomics 2012, 13:192 Download PDF Background: In rubber tree, bark is one of important agricultural and biological organs. However, the molecular mechanism involved in the bark formation and development in rubber tree remains largely unknown, which is at least partially due to lack of bark transcriptomic and genomic information.…

[cbirt]

A thorough understanding of the cellular connections inside these tissues is being made possible by technologies that use spatial transcriptomic approaches to revolutionize the study of ligand-receptor signaling in tissues. CytoSignal was created by researchers from the University of Texas and the University of Michigan to use spatial transcriptomic data to infer the locations and dynamics of cell-cell communication at the cellular level. 

CytoSignal offers a basic understanding of the spatial dynamics of signaling connections. It finds differentially expressed genes, measures contact-dependent and diffusible interactions, and locates geographic gradients in signaling strength. Numerous spatial transcriptomic approaches, such as spot-based protocols without deconvolution and FISH-based techniques, are compatible with CytoSignal. The tool’s outcomes are verified in situ using a proximity ligation assay, which shows that tissue locations of ligand-receptor protein-protein interactions closely correspond with CytoSignal scores. The current necessity for cellular resolution detection of cell-cell signaling connections and their dynamics is met by this dependable and scalable method.

In multicellular animals, cell-cell communication is an essential mechanism that requires the dimerization of membrane-bound proteins or the binding of secreted ligands to transmembrane receptors. Differentiation, destiny selection, immunological response, growth, and physiological tissue function depend on this communication. Finding the signaling relationships between cells in particular situations is still difficult, though. Some progress has been made by elucidating the expression of ligands and receptors by cell type within diverse tissues using single-cell RNA sequencing (scRNA) datasets. Techniques for inferring cell-cell communication from single-cell RNA data have been reported, such as CellPhoneDB, CellChat, NicheNe, SingleCellSignalR, and Scriabin. However, these methods lack information regarding cell spatiality and cannot be used to infer signaling among cell groups.

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[mindshelter]

i've seen that sm2099 letter to the editor by that then-molecular genetics graduate student circulate lately (since it gives a pretty good explanation as to how the changes miguel underwent should effect him, and how, as a chimera of sorts, he no longer meets the scientific defintion of human) and i realize no one reacted like i did which was simply nod along to and go, "yep," before immediately finding the guy on linkedin to see that he's still a cancer researcher who enjoys comics, almost three decades later. and that rocks

[grrlscientist]

Diverse Headgear Of Hoofed Mammals Evolved From A Common Ancestor, study Baruch College & CUNY Graduate Center, published by Communications Biology

by @GrrlScientist

[marketprofessional]

[elucidata]

GEO Datasets for Transcriptomics Meta-Analysis: Unlocking Hidden Insights

Meta-analysis is a powerful statistical method that enables researchers to combine and analyze data from multiple studies, providing a broader perspective and more robust findings. In transcriptomics research, where the focus is on studying gene expression patterns, meta-analysis plays a crucial role in uncovering molecular signatures that may not be apparent in single studies due to limited sample sizes or variability.

This blog aims to empower transcriptomics researchers by providing insights into effective meta-analysis techniques. By leveraging available transcriptomics databases like GEO (Gene Expression Omnibus), ArrayExpress, and SRA, researchers can enhance their investigations and contribute to scientific progress. Polly, a tool designed to enhance data usability, helps make this data actionable, enabling researchers to streamline their analyses and gain deeper insights.

GEO Datasets for Impactful Meta-Analysis

The Gene Expression Omnibus (GEO) is an invaluable resource for transcriptomics, offering a vast array of publicly available gene expression data, including microarrays, RNA sequencing, etc. GEO facilitates global data sharing, enabling researchers to explore gene expression patterns, uncover molecular mechanisms, and investigate links to diseases. This collaborative platform encourages data reuse, scientific discovery, and open sharing within the genomics community.

Original Publish

[bpod-bpod]

More Than Immunity

As skin develops before birth it's rich in innate (a first response, rather than an adapting form of defence) immune cells, including macrophages. By creating a reference atlas of pre-natal human skin (7–17 weeks post-conception), combining single-cell and spatial transcriptomics (locating active genes) data, this study finds that macrophages play a role beyond providing immunity, driving the development of vessels in the skin

Read the published research article here

Still from video from work by Nusayhah Hudaa Gopee, Elena Winheim and Bayanne Olabi, and colleagues

Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

Video originally published with a Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0)

Published in Nature, October 2024

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[healthcarehubhh]

Spatial Genomics Transcriptomics: A Novel Method for Analyzing Cellular Heterogeneity

Spatial genomics transcriptomics is an emerging single-cell sequencing technique that allows for the measurements of gene expression across spatially localized regions of a tissue. Unlike traditional single-cell RNA sequencing approaches that dissociate tissues into single cells before analysis, spatial genomics transcriptomics retains the spatial architecture and cellular context of the original tissue. This property allows researchers to map gene expression profiles onto precise locations in tissues and identify cell types in various anatomical regions. How Does it Work? At the core of the technology is a hydrogel-based tissue immobilization method. Tissues are frozen and embedded in a hydrogel matrix. The hydrogel stabilizes cell positions relative to one another during subsequent processing steps. The embedded tissue is then sliced into thin sections and mounted onto a glass slide. Oligonucleotide-conjugated barcodes are arrayed on the slide in distinct spots. When the tissue section is placed on top, cells come into contact with the array spots, with each spot representing a discrete location in the original tissue. Cellular mRNAs are released, diffuse through the hydrogel, and hybridize to complimentary barcodes. The slide is then subjected to reverse transcription and library preparation for sequencing. In this way, DNA sequences representing the transcriptomes of cells from defined locales are generated and spatially mapped. Data Analysis and Visualization The sequenced libraries contain both positional barcode and gene expression information which can be analyzed using computational techniques. Spatial gene expression maps of the original tissue are reconstructed by aligning the sequencing reads back to the original positional barcode array. This data can then be analyzed with various clustering and dimensionality reduction algorithms to identify regionalized cell populations and characterize their transcriptomic signatures. Spatial expression patterns are often visualized as "heatmaps" - with gene expression abundance levels represented by a color gradient across the tissue area. Various bioinformatics tools have also been developed to integrate spatial transcriptomics data with other omics data types, annotations, and cellular atlases - allowing researchers to compare expression profiles against known cell types and phenotypes. Applications and Insights In the past few years, spatial genomics transcriptomics has offered new perspectives on tissue organization and enabled discoveries that conventional methods could not. For example, studies have mapped immune cell infiltration patterns in tumor microenvironments with single-cell resolution. This has provided clues about how the interplay between tumors and immune responses impact clinical outcomes. In the brain, spatial transcriptomics has revealed molecular definitions of cortical layers and subregions, characterized progenitor cell zones in the hippocampus, and tracked neural cell maturation across development. By preserving spatial relationships, it has also facilitated discoveries like gradients of gene expression correlating with tissue architecture in the skin. Researchers are also exploring its potential in fields like developmental biology, neuroscience, immunology and more - to decipher how tissues are patterned, gain insights into disease progression and responses to therapies, and map cell-cell communication networks at a fine-scale level in intact native environments. As protocols evolve to incorporate additional readouts like protein localization, spatial genomics promises to revolutionize our multi-dimensional understanding of tissue organization and function.

[healthlogic]

RNA Analysis/Transcriptomics Market Partnering Deals of Key Players 2024 – 2031

The Insight Partners market research RNA Analysis/Transcriptomics Market Size and Share Report | 2031 is now available for purchase. This report offers an exclusive evaluation of a range of business environment factors impacting market participants. The market information included in this report is assimilated and reliant on a few strategies, for example, PESTLE, Porter's Five, SWOT examination, and market dynamics

RNA Analysis/Transcriptomics market is evaluated based on current scenarios and future projections are added keeping the projected period in consideration. This report integrates the valuation of RNA Analysis/Transcriptomics market size for esteem (million USD) and volume (K Units). Research analysts have used top-down, bottom-up, primary, and secondary research approaches to evaluate and approve the RNA Analysis/Transcriptomics market estimation.

Detailed scrutiny of market shares, optional sources, and basic essential sources has been done to integrate only valid facts. This research further reveals strategies to help companies grow in the RNA Analysis/Transcriptomics market.

Key objectives of this research are:

To contemporary market dynamics including drivers, challenges, threats, and opportunities in the RNA Analysis/Transcriptomics market.

To analyze the sum and market estimation of the worldwide RNA Analysis/Transcriptomics market

Based on key facets, market segments are added.

The competitive analysis covers key market players and their business strategies.

To examine the RNA Analysis/Transcriptomics Market for business probable and strategic outlook.

To review the RNA Analysis/Transcriptomics Market size, key regions and countries, end-users, and statistical details.

To offer strategic recommendations based on the latest market developments, and RNA Analysis/Transcriptomics market trends.

Perks of The Insight Partners’ RNA Analysis/Transcriptomics Market Research

Market Trends: Our report reveals developing RNA Analysis/Transcriptomics market trends that are poised to reshape the market preparing businesses with the foresight to retain their competitive edge. This Market research report presents market trends, supply chain analysis, leading participants, and business growth strategies. This research covers technological progress and key developments covering various aspects of the inclusive market. It is valuable market research for existing key players as well as new entrants in the RNA Analysis/Transcriptomics Market. Through inputs derived from experts, this research attempts to guide future investors about market details and potential returns on investment. 

Competitive Landscape: This research reveals key market players, their strategies, and possible areas for differentiation.

Analysts Viewpoint: We have industry-specific experts who add credibility to this report with their exclusive viewpoints based on market understanding and expertise. This report goes further into details of entire business processes and doesn’t restrict to only operational aspects. These insights cover venture economics and include tactics for capital investment, investor funding, and projections of ROIs.  Net income and profit loss financial stats are crucial metrics of this RNA Analysis/Transcriptomics market report. With these meticulous insights companies can reduce their risks and increase the success rate in the coming decade. 

RNA Analysis/Transcriptomics Market Report Coverage:

Report Attributes

Details

Segmental Coverage

Product

Consumables

Instruments

Software Services

Technology

Microarray

Real-Time Quantitative Polymerase Chain Reaction

Sequencing Technologies

Application

Diagnostics Disease Profiling

Drug Discovery Others

End User

North America

Europe

Asia Pacific

South Central America

and Geography

Regional and Country Coverage

North America (US, Canada, Mexico)

Europe (UK, Germany, France, Russia, Italy, Rest of Europe)

Asia Pacific (China, India, Japan, Australia, Rest of APAC)

South / South & Central America (Brazil, Argentina, Rest of South/South & Central America)

Middle East & Africa (South Africa, Saudi Arabia, UAE, Rest of MEA)

Market Leaders and Key Company Profiles

BioRad Laboratories Inc.

Illumina Inc.

GE Healthcare

F. HoffmannLa Roche Ltd.

Agilent Technologies Inc.

Thermofisher Scientific Inc.

Sigma Aldrich

Qiagen N.V.

Affymetrix Inc.

Fluidigm Corporation

Other key companies 

What all adds up to the credibility of this research?

A comprehensive summary of the contemporary RNA Analysis/Transcriptomics market scenario

Precise estimations on market revenue forecasts and CAGR to rationalize resources

Regional coverage to uncover new markets for business

Rivalry analysis aims to help corporations at a modest edge

Facts-based crystal-clear insights for business success

The research can be customized as per business necessities

Access to PDF, and PPT formats of this research

About Us:

The Insight Partners is a one-stop industry research provider of actionable intelligence. We help our clients in getting solutions to their research requirements through our syndicated and consulting research services. We specialize in industries such as Semiconductor and Electronics, Aerospace and Defense, Automotive and Transportation, Biotechnology, Healthcare IT, Manufacturing and Construction, Medical Devices, Technology, Media and Telecommunications, Chemicals and Materials.

Contact Us: www.theinsightpartners.com

[wmhd]

In silico transcriptome screens identify epidermal growth factor receptor inhibitors as therapeutics for noise-induced hearing loss.

Noise-induced hearing loss (NIHL) is a common sensorineural hearing impairment that lacks U.S. Food and Drug Administration–approved drugs. To fill the gap in effective screening models, we used an in silico transcriptome-based drug screening approach, identifying 22 biological pathways and 64 potential small molecule treatments for NIHL. Two of these, afatinib and zorifertinib [epidermal growth factor receptor (EGFR) inhibitors], showed efficacy in zebrafish and mouse models. Further tests with EGFR knockout mice and EGF-morpholino zebrafish confirmed their protective role against NIHL. Molecular studies in mice highlighted EGFR’s crucial involvement in NIHL and the protective effect of zorifertinib. When given orally, zorifertinib was found in the perilymph with favorable pharmacokinetics. In addition, zorifertinib combined with AZD5438 (a cyclin-dependent kinase 2 inhibitor) synergistically prevented NIHL in zebrafish. Our results underscore the potential for in silico transcriptome-based drug screening in diseases lacking efficient models and suggest EGFR inhibitors as potential treatments for NIHL, meriting clinical trials.

Read the full research paper!

[visionresearchreport]

The spatial transcriptomics market is experiencing a significant surge driven by advancements in technology and increasing applications across various research domains. This innovative approach allows researchers to visualize gene expression within the context of tissue morphology, providing invaluable insights into complex biological systems. With the ability to analyze gene expression patterns in their spatial context, researchers can unravel intricate molecular mechanisms underlying diseases and developmental processes.

[cbirt]

The introduction of NGS transformed the field of biology, allowing for greater insights into biological mechanisms through the gathering of rich, accessible data. However, important context is lost due to the data being conventionally stored in tabular formats, which erase the impacts that spatial connectivity has on genes and gene expression. A new framework, GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), developed by the Aarhus University researchers, shows that the incorporation of this crucial information may lead to improved performance in neural network models.

The invention of next-generation sequencing has entirely transformed the field of genetics through a combination of lowered costs and extensive data generation. NGS techniques are often performed repeatedly on samples in order to derive data about different aspects of the biological architecture, which can then be integrated to produce holistic conclusions about the samples being studied.

It is known that the data within the genome is organized in a spatial manner, positioned sequentially on the length of the chromosomes. However, genomic data derived through NGS is stored in a tabular manner instead, resulting in the loss of the spatial connectivity that occurs naturally in organisms.

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