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Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Digital spatial profiling for pathologists.

Benedetta Donati1, Gloria Manzotti1, Federica Torricelli1

  • 1Laboratory of Translational Research, Arcispedale Santa Maria Nuova, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Via Risorgimento 80, 42124, Reggio Emilia, Italy.

Virchows Archiv : an International Journal of Pathology
|November 5, 2024
PubMed
Summary
This summary is machine-generated.

Digital Spatial Profiler (DSP) enables pathologists to spatially profile tumors in situ, combining morphology with genomic analysis. This technology reveals complex tumor heterogeneity across various cancer types, enhancing diagnostic capabilities.

Keywords:
Digital spatial profilingHematoxylin–eosinSurgical pathologyTissue slides

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Area of Science:

  • Cancer research
  • Pathology
  • Genomics
  • Spatial biology

Background:

  • "Omics" technologies offer deep tumor profiling but struggle to capture full tumor complexity and spatial context.
  • Pathologists prefer in situ assessments, like immunohistochemistry, over bulk profiling methods.
  • Existing methods often fail to integrate morphological and genomic data effectively.

Purpose of the Study:

  • To evaluate the feasibility and utility of the NanoString GeoMx® Digital Spatial Profiler (DSP) from a pathologist's viewpoint.
  • To demonstrate DSP's capability in multiplex profiling of tissue slides guided by morphology.
  • To showcase DSP's application in diverse neoplastic settings: breast carcinoma, anaplastic thyroid carcinoma, and biphasic mesothelioma.

Main Methods:

  • Applied NanoString GeoMx® Digital Spatial Profiler (DSP) on tissue slides from three distinct cancer types.
  • Utilized morphology-guided selection of regions of interest on hematoxylin-eosin stained slides.
  • Performed multiplex profiling to digitally quantify target analytes in situ within specific tissue compartments.

Main Results:

  • Successfully distinguished tumor cells from the microenvironment in breast carcinoma using two antibodies.
  • Differentiated between epithelioid and sarcomatoid components in biphasic mesothelioma.
  • Effectively separated anaplastic and well-differentiated areas in thyroid carcinoma.

Conclusions:

  • Digital Spatial Profiler (DSP) is a promising tool integrating traditional histology with in situ digital profiling.
  • DSP allows spatial assessment of tumors and their microenvironments, overcoming limitations of bulk genomic methods.
  • Pathologists can leverage DSP to combine morphological and genomic analyses for advanced cancer progression studies.