#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

You can also follow BPoD on Instagram, Twitter and Facebook

[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.

Continue Reading

[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

[acumenblog]

Spatial Genomics and Transcriptomics Market Sales and Revenue Report 2022-2030

The Spatial Genomics and Transcriptomics Market report offers an in-depth analysis of the global market landscape, examining its historical performance, current dynamics, and anticipated future trends. It provides a detailed overview of key market drivers, growth patterns, and emerging trends, shedding light on the evolving landscape of the Spatial Genomics and Transcriptomics industry. The…

[prachicmi]

New Frontiers in Healthcare: Understanding Diseases at the Single Cell Analysis

Advances in Single Cell Analysis Transform Disease Understanding Single cell analysis techniques allow researchers to study biological systems at an unprecedented resolution by analyzing individual cells. Earlier research relied on studying cells in bulk which averaged effects across thousands to millions of cells obscuring subtle differences between individual cells. Single cell analysis ismethods have revolutionized our understanding of complex diseases like cancer by revealing significant heterogeneity present even within the same tumor. Pharmaceutical companies are leveraging these new insights to develop more targeted treatments. Single Cell Transcriptomics Revolutionizes Cancer Research Single Cell Analysis intranscriptomics refers to studying which genes are turned on or off in a cell. Traditional transcriptomic approaches study populations of thousands of cells together losing cell-to-cell variations. Single cell transcriptomics analyzes gene expression profiles of individual cells. When applied to study of cancer it has revealed substantial diversity present even within the same tumor with different cell subpopulations driving different aspects of tumor progression and metastasis. This challenges the concept of cancer as a single homogeneous entity. Understanding the behavior of distinct cell types driving a tumor is crucial for developing more effective combination therapies. Pharma companies are using single cell transcriptomics to stratify patients and guide development of personalized treatment regimens. Proteomics Adds New Dimension to Single Cell Analysis While transcriptomics studies gene activity, proteomics analyzes the proteins actually present and functioning in a cell. Single cell proteomics additionally characterizes post-translational protein modifications and interactions not captured by transcript data. When combined with transcriptomics, proteomics provides a more complete picture of cellular states and phenotypes. Researchers have used this multi-omics approach on tumor tissues to not just classify tumor types but also to predict prognosis and guide treatment selection. Pharma companies are applying single cell proteomics techniques during drug development to better understand mechanisms of drug action and resistance at a single cell level. This improves their ability to design combination therapies.

Get more insights on, Single Cell Analysis

For Deeper Insights, Find the Report in the Language that You want.

Japanese Korean

About Author:

Vaagisha brings over three years of expertise as a content editor in the market research domain. Originally a creative writer, she discovered her passion for editing, combining her flair for writing with a meticulous eye for detail. Her ability to craft and refine compelling content makes her an invaluable asset in delivering polished and engaging write-ups.

(LinkedIn: https://www.linkedin.com/in/vaagisha-singh-8080b91)

[bpod-bpod]

Abreast with Cells

Characterisation of the 12 cell types that make up the human breast by profiling their gene activity (transcriptome). This analysis provides a resource which both distinguishes them and reveals the biological processes in which they participate

Read the published research article here

Image from work by Katelyn Del Toro and Rosalyn Sayaman, and colleagues

Department of Biochemistry and Molecular Biology, University of New Mexico School of Medicine, University of New Mexico, Albuquerque, NM, USA

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

Published in PLOS Biology, November 2024

You can also follow BPoD on Instagram, Twitter and Facebook

[biomededgene]

Python-Based Pipelines for Variant Calling in Genomic Research

In the rapidly evolving field of genomics, variant calling is a cornerstone of genomic research that enables scientists to identify genetic variations associated with diseases, traits, and evolutionary processes. With the exponential growth of high-throughput sequencing technologies, researchers are now faced with vast amounts of genomic data, necessitating efficient and robust tools for analysis. Python, a versatile programming language with a rich ecosystem of libraries, has become a popular choice for developing variant calling pipelines. In this article, we will explore how to create Python-based pipelines for variant calling, focusing on the process of handling VCF files, and illustrating how Python can transform genomic data into meaningful discoveries.

To know more, visit us at: https://edgenebiomed.com/

[industrynewsupdates]

Global Europe, Cis & Africa Spatial Transcriptomics Market: Insights and Market Forecast

The Europe, CIS & Africa spatial transcriptomics market was valued at approximately USD 93.9 million in 2023 and is expected to experience robust growth, with a projected compound annual growth rate (CAGR) of 15.57% from 2024 to 2030. Several key factors are contributing to this growth, including the increasing recognition of spatial omics analysis in cancer research, the introduction of fourth-generation sequencing technologies (such as in-situ sequencing), and a surge in funding and collaborative initiatives aimed at advancing spatial biology research.

A primary driver behind the market expansion is the rising prevalence of cancer. As cancer rates continue to climb, there is an increasing demand for more effective approaches to biomarker discovery, early detection, and precise diagnostics. These advancements are critical for better disease management and more targeted treatment options. Spatial omics analysis, particularly in the field of oncology, has shown considerable promise in addressing these needs. It offers a more nuanced understanding of tumor heterogeneity, supports the identification of potential biomarkers, and helps inform personalized treatment strategies that can be tailored to individual patients.

One of the major advantages of spatial omics technologies is their ability to map the spatial distribution of various cell types within a tumor. This feature is vital for studying the heterogeneity of tumors, which can significantly affect treatment outcomes. A relevant example of this is a study published in Nature in May 2024, where researchers employed both spatial and single-cell transcriptomics to explore the molecular interactions and tumor heterogeneity in colorectal cancer (CRC). By using these advanced techniques, the researchers were able to gain deeper insights into the underlying mechanisms driving CRC progression, showcasing the potential of spatial transcriptomics in improving our understanding of complex diseases like cancer.

Gather more insights about the market drivers, restrains and growth of the Europe, Cis & Africa Spatial Transcriptomics Market

Country Insights

Europe led the spatial transcriptomics market in 2023, commanding a dominant share of 94.57%. This dominance can be attributed to the region's well-established biotechnology research and development (R&D) sector, its growing emphasis on spatial biology, and the presence of leading industry players. Additionally, substantial investments and funding from both public and private entities have significantly contributed to advancing spatial transcriptomics research and facilitating the commercialization of spatial omics products, further driving market growth.

United Kingdom (UK):

The spatial transcriptomics market in the UK is anticipated to experience significant growth in the coming years. This is largely due to the continuous technological advancements in spatial biology, which are increasingly being applied across various fields such as oncology, neurology, and personalized medicine. A key example of the UK's role in fostering innovation is the 12th Annual Single Cell & Spatial Analysis UK Congress, part of Next Gen Omics 2024, scheduled for October 23-25, 2024, in London. This prominent event will bring together leading experts from around the world to discuss the latest developments, cutting-edge technologies, and future prospects of spatial biology, underscoring the UK's position as a hub for research and innovation in the field.

Germany:

Germany's spatial transcriptomics market is experiencing significant growth, particularly within the broader multi-omics field. The country benefits from active engagement by renowned academic institutions, leading biotechnology and pharmaceutical companies, and substantial government-backed research funding. For example, in early 2024, the European Molecular Biology Laboratory (EMBL) hosted a series of events and training courses, focusing on integrating and analyzing multiomics data, further enhancing the country's position in spatial transcriptomics and related fields.

France:

In France, the spatial transcriptomics market is also set to witness strong growth, driven by the increasing adoption of advanced genomic technologies and their expanding applications across diverse fields such as cancer research, drug discovery, and translational research. Moreover, the French government’s continued investment in genomic research initiatives is providing a solid foundation for the market’s development, fostering both innovation and collaboration in the space.

Commonwealth of Independent States (CIS) Market Trends

The CIS region, which includes countries such as Russia, Ukraine, Belarus, and Kazakhstan, is expected to see growth in the spatial transcriptomics market. This growth is largely fueled by an increased adoption of advanced genomics and transcriptomics technologies, a growing focus on spatial biology research, and rising demand for more detailed insights into cellular function and organization. These CIS countries are home to several well-established research institutions and academic centers with deep expertise in molecular biology and biotechnology, providing a strong foundation for growth in spatial transcriptomics research and applications.

Africa Spatial Transcriptomics Market Trends

South Africa is expected to experience significant growth in the spatial transcriptomics market, driven by an increased demand for improved diagnostic tools that support disease prevention and treatment. The country’s growing healthcare sector, combined with ongoing advancements in genomic technologies, creates a promising landscape for the adoption of spatial transcriptomics.

In contrast, the Nigerian market for spatial transcriptomics remains in its early stages. While there is potential for growth, the market faces challenges due to the high costs associated with specialized equipment and reagents. Additionally, the need for skilled labor to operate these advanced technologies represents another potential barrier to rapid market development. As such, significant investment in infrastructure, training, and research capacity will be necessary to accelerate market growth in Nigeria and other parts of West Africa.

Browse through Grand View Research's Biotechnology Industry Research Reports.

• The global exosomes market size was estimated at USD 177.4 million in 2024 and is anticipated to grow at a CAGR of 28.73% from 2025 to 2030. 

• The global cell culture media storage containers market size was estimated at USD 2.11 billion in 2024 and is projected to witness a CAGR of 12.55% from 2025 to 2030. 

Key Europe, CIS & Africa Spatial Transcriptomics Company Insights

Several key players in the spatial transcriptomics market are actively pursuing strategies to strengthen their market presence and expand the reach of their products and services. These strategies primarily include expansion activities and strategic partnerships aimed at advancing research, increasing the commercialization of spatial omics products, and enhancing collaborations between academia and industry. Through these initiatives, companies are not only boosting their market footprint but are also contributing to the broader advancement of spatial biology and transcriptomics technologies.

Key Europe, CIS & Africa Spatial Transcriptomics Companies:

• Illumina, Inc.

• Bruker

• 10X Genomics

• EdenRoc Sciences (Cantata Bio, LLC)

• Shimadzu Corporation

• Waters Corporation

• Bio-Techne

• Vizgen Inc.

• Spatial Genomics

• Akoya Biosciences, Inc

Order a free sample PDF of the Europe, Cis & Africa Spatial Transcriptomics Market Intelligence Study, published by Grand View Research.

[navyasri1]

RNA Analysis/Transcriptomics Market - Forecast(2024 - 2030)

Transcriptomics/RNA analysis can be defined as the study of the transcriptome or the complete set of RNA transcripts which are produced by the genome, under specific circumstances, environment or in a specific cell - using high-throughput methods, such as microarray analysis. Globally increasing R&D activities in RNA sequencing and trascriptomics is expected to remain key growth driver for the RNA analysis market during the period of study.