13
 NOV

13. November 2023

Immunofluorescence Image Analysis: The Future of Tissue Cytometry


In biomedical research, techniques like immunofluorescence staining combined with tissue cytometry have emerged as powerful tools for studying cellular structures, protein expression, and interactions in-situ within tissue samples. These techniques provide valuable insights into cellular function and spatial distribution within the context of their tissue environment. This blog post will discuss the basics of immunofluorescence, the importance of follow-up image analysis, and the role of image-based cytometry in shaping the future of tissue cytometry.

Basics of Immunofluorescence

It is a data-driven approach that utilises advanced technologies, such as genomic sequencing, imaging, and machine learning, to gather and analyse large amounts of patient data. This data is then used to identify specific genetic mutations or protein expression patterns that are unique to each patient, and to develop personalised treatment plans that target those mutations or expression levels.

Multiplex Immunofluorescence and Its Techniques

Multiplex immunofluorescence staining takes IF to a new level by enabling the detection of multiple proteins at once in a single tissue section. By increasing the number of markers through multiple staining cycles (CycIF), combined with rounds of imaging and follow-up coregistration of the images, this technique is able to provide an in-depth  understanding of the cellular microenvironment and interactions within the tissue.2 Due to the increased amount of collected data, image analysis algorithms further support researchers in analyzing multiplex immunofluorescence images, enabling them to quantify and characterize labeled structures within the tissue.3 The combination of scanning stained tissue sections and image analysis is the definition of tissue cytometry.

Tissue Cytometry: Whole Slide Scanning and Image Analysis

Whole slide imaging, also known as virtual microscopy or digital pathology, is the first component of tissue cytometry. During the process, researchers scan a tissue section on a slide to create a digital reconstruction of the entire stained tissue. This digitized information enables visual examination on digital slides, providing flexibility and portability. The second component, image analysis, is important to better understand critical cellular and spatial interactions within their normal context. Tissue cytometry enhances the accuracy, efficiency, and reproducibility of cellular imaging studies.

Tissue Cytometry and FISH

One example of next-generation pathology being enhanced by tissue cytometry is fluorescence in-situ hybridization (FISH). This process is used to determine abnormalities in single chromosomes in research and digital diagnostic pathology and can be streamlined through tissue cytometry and TissueGnostics’ products

Tissue Cytometers from TissueGnostics

Companies like TissueGnostics are at the forefront of providing next-generation automated tissue cytometry solutions, supporting organizations in their biomarker discovery and quantification endeavors. With the continuous development of imaging techniques, machine learning algorithms, and image analysis tools, the possibilities for tissue cytometry are endless.

TissueFAXS Q & SL Q Systems

As a scientific expert, staying updated with the advancements in immunofluorescence image analysis and its role in tissue cytometry is vital. By embracing new technologies, such as TissueGnostics´ TissueFAXS tissue cytometers, researchers can access automated, whole-slide imaging solutions to enhance their analyses. These systems offer high-speed, multi-channel fluorescence and brightfield imaging in various configurations and are available in inverted and upright options with LED-based technology and slide ID scanners to support high-throughput of slides and fast imaging.

Additionally, TissueGnostics has developed an image analysis suite that offers, among other tools, deep-learning-based nuclei segmentation and tissue classifiers based on machine learning.

Contact a member of TissueGnostics today to learn more about immunofluorescence image analysis.

References

  1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918834/ 
  2. https://onlinelibrary.wiley.com/doi/full/10.1002/cac2.12023
  3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271766/
  4. https://www.news-medical.net/news/20180904/Image-Cytometry-Technology-and-Tissue-Analysis.aspx

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+43 1 216 11 90
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