We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ZeptoMetrix an Antylia scientific company

Download Mobile App




Spatial Transcriptomics Technique Enables Visualization of Gene Expression in Tissues

By LabMedica International staff writers
Posted on 12 Jul 2016
A team of Swedish researchers has developed a high-resolution method for resolving the transcriptome – the library of active genes and RNAs – in histological samples of patients' tissues.

Analysis of the pattern of proteins or messenger RNAs (mRNAs) in histological tissue sections is a vital aspect of biomedical research and diagnostics. More...
Due to the complexities of current techniques, this typically involves the visualization of only a few proteins or expressed genes at a time.

Investigators at the Karolinska Institutet (Stockholm, Sweden) and the Royal Institute of Technology (Stockholm, Sweden) described a method for visualization and quantitative analysis of the complete transcriptome with spatial resolution in individual tissue sections.

The method, which they called "spatial transcriptomics", relied on positioning histological sections onto arrayed reverse transcription primers with unique positional barcodes. Reverse transcription was followed by sequencing and computational reconstruction, and this could be done for multiple genes simultaneously. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, the investigators demonstrated high-quality RNA-sequencing data with maintained two-dimensional positional information from mouse brain and human breast cancer.

"By placing tissue sections on a glass slide on which we have placed DNA strands with built in address labels we have been able to label the RNA molecules formed by active genes," said senior author Dr. Jonas Frisén, professor of stem cell research at the Karolinska Institutet. "When we analyze the presence of RNA molecules in the sample, the address labels show where in the section the molecules were and we can get high-resolution information on where different genes are active. It makes it possible to study which genes are active in tissues with greater resolution and precision than ever before, which is valuable to both basic research and diagnostics."

The method was described in detail in a paper published in the July 1, 2016, issue of the journal Science.

Related Links:
Karolinska Institutet
Royal Institute of Technology

Gold Member
Troponin T QC
Troponin T Quality Control
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Blood Gas and Chemistry Analysis System
Edan i500
New
Pipet Controller
Stripettor Pro
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Clinical Chemistry

view channel
Image: The GlycoLocate platform uses multi-omics and advanced computational biology algorithms to diagnose early-stage cancers (Photo courtesy of AOA Dx)

AI-Powered Blood Test Accurately Detects Ovarian Cancer

Ovarian cancer ranks as the fifth leading cause of cancer-related deaths in women, largely due to late-stage diagnoses. Although over 90% of women exhibit symptoms in Stage I, only 20% are diagnosed in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Pathology

view channel
Image: Schematic diagram of multimodal single-cell MSI using tapping-mode scanning probe electrospray ionization (Photo courtesy of Yoichi Otsuka)

New Technology Improves Understanding of Complex Biological Samples

Tissues are composed of a complex mixture of various cell types, which complicates our understanding of their biological roles and the study of diseases. Now, a multi-institutional team of researchers... Read more

Technology

view channel
Image: The new algorithms can help predict which patients have undiagnosed cancer (Photo courtesy of Adobe Stock)

Advanced Predictive Algorithms Identify Patients Having Undiagnosed Cancer

Two newly developed advanced predictive algorithms leverage a person’s health conditions and basic blood test results to accurately predict the likelihood of having an undiagnosed cancer, including ch... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.