Case 1 – Lipids in Cancer
1. How can you study the molecular changes and tissue heterogeneity in tumor tissues?
What is tumor heterogeneity?
Tumors are highly heterogeneous in composition, including cellular content, genetic, epigenetic,
protein, and differentiation.
Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic
profiles expressed as diverse molecular, morphological and spatial distributions.
Existing approaches to assess metabolic functions of tumors generally do so at tissue-scale
resolution. As such, the cellular basis for signals of metabolic heterogeneity in tumors has not
been completely elucidated.
This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient
treatment.
Histology
Histology is the study of the microscopic anatomy of cells and tissues of organisms. Histological
analysis is performed by examining a thin slice (section) of tissue under a light (optical) or electron
microscope.
In histology image analysis for cancer diagnosis, histopathologists visually examine the
regularities of cell shapes and tissue distributions, decide whether tissue regions are cancerous,
and determine the malignancy level.
Limitations of histology
The use of histology, molecular, and imaging data is currently the gold standard for the clinical
diagnosis of cancers and identification of prognostic and therapeutic targets. Tissue evaluation of
stained or labeled slides is traditionally performed manually by highly trained histopathologists. In
recent years, computer-assisted diagnosis (CAD) systems have been implemented to aid
histopathologists and clinicians in cancer diagnosis and research, which significantly reduce labor
requirements and subjectivity in analysis. However, H&E stained images only provide tissue
morphology information and may not resolve healthy cells from early-stage cancerous cells when
changes may be purely biochemical in nature.
Accuracy of histology delineation using traditional approaches can be problematic. For example,
delineation of pancreatectomy margins can be variable and subjective, with false-negative results
occurring in up to 20% to 30% of pancreatic adenocarcinoma patients, usually leading to additional
surgical intervention. Hence, there is a requirement for more specific biochemical markers that can
be used to accurately delineate tumor boundaries preventing the potential for tumor re-emergence
and minimizing complications caused by excessive removal of healthy tissue.
Biomarkers are “a characteristic that is objectively measured and evaluated as an indicator of normal
biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
In clinical laboratories, imaging biomarkers are utilized for disease diagnosis and grading and
evaluating therapeutic effect and/or toxicity. In research settings, biomarkers are used in
pathogenesis and drug discovery and studies to investigate pathophysiological effect and efficacy.
Traditional imaging methodologies such as immunohistochemical labeling or staining combined with
fluorescence microscopy allow for the visualization of tumor biomarkers and tissue structures with
high specificity and at high spatial resolution. However, these methods are targeted, requiring prior
,knowledge about the analyte (lose important spatial information on the analyte) to be studied, and
are limited by the small number of molecules that can be visualized simultaneously.
Mass spectrometry
Mass spectrometry can generate profiles that contain hundreds of biomolecules directly from tissue.
Spatially correlated mass spectrometry, imaging MS, reveals how each biomolecular ion varies across
tissue samples. By combining imaging MS with histology, MS profiles of specific pathological entities
have been used to identify biomarkers of disease, and when combined with clinical outcomes identify
signatures associated with prognosis and response to therapy.
Using mass spectrum, you can visualize where the compounds are present in the sample
The more scale towards red, the higher the concentration of the compound
Have information of what is in the tissue and where it is in the tissue
Mass spectrometry to measure lipids in tumor tissue
Due to the lack of methods to examine lipid content, trafficking, and distribution, several techniques,
including mass spectrometry, have become essential for a comprehensive examination of the
distribution, functions, and consequences of lipid variation in the context of disease. Mass
spectrometry imaging has rapidly risen to become a premier technology for lipid imaging in biological
tissues and cells owing to its high specificity, sensitivity, and offered increasing spatial capabilities.
, Mass spectrometry imaging is a valuable tool for the lipidomic analysis of cancer tissue as it adds
the specificity of MS to detailed spatial information.
MSI relies on the desorption of molecules present on the surface of a solid flat sample.
In MSI, not only are intensities of mass to charge (m/z) values recorded but also the respective
positions within the sample allowing for the generation of images representing ion intensities at
specific tissue localizations.
This information is of particular interest when analytes such as lipids are not homogeneously
distributed but compartmentalized in tissue.
Mass spectrometry is required to determine the exact fatty acid composition of the lipid. The
fatty acid composition of lipids in tumors has been shown to differ substantially from surrounding
healthy tissue, and fatty acid metabolism has been shown to correlate with malignant phenotypes
including metastasis, therapeutic resistance and relapse.
Staining vs. MSI Workflow
Typical mass spectrometry workflow
, Biopsy specimens are collected, snap frozen, and sectioned onto compatible slides using a cryostat. MS images are acquired by rastering the
ionizing beam across the tissue surface. Ion distribution maps of lipids are reconstructed using software. Multivariate statistical analysis is
performed to identify candidate biomarker lipids or lipid signatures of tumors. Histological staining is performed on the sam e or adjacent
tissue section. MS imaging dataset is coregistered with optical image and candidate biomarkers are correlated with histologically defined
regions or cell populations. Potential biomarkers have multiple applications in cancer research.
A typical MSI experiment is performed by rastering an ionization beam across a tissue surface at
defined x,y coordinates, thereby desorbing/ionizing analytes that are subsequently extracted into the
mass spectrometer for analysis. Software is used to reconstruct an image from the dataset in which
each pixel consists of a mass spectrum. Images can be plotted for any selected peak in the acquired
mass spectrum with the relative abundances displayed as a false-color image across the analyzed
region of interest.
Normalization is critically important for the proper interpretation of MSI datasets. Normalization is
the process of multiplying a mass spectrum with an intensity-scaling factor to expand or reduce the
range of the intensity axis. It is used to remove instrumental and systemic artifacts that impact signal
intensities by projecting spectra of varying intensity onto a common intensity scale.Artifacts may
originate from matrix application, matrix crystal heterogeneity, or ion source contamination.
Ionizing Modalities
The three most commonly used MSI ion sources are MALDI MS (matrix-assisted laser
desorption/ionization mass spectrometry), DESI-MS (desorption electrospray ionization mass
spectrometry), and SIMS (secondary ion mass spectrometry).
1. How can you study the molecular changes and tissue heterogeneity in tumor tissues?
What is tumor heterogeneity?
Tumors are highly heterogeneous in composition, including cellular content, genetic, epigenetic,
protein, and differentiation.
Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic
profiles expressed as diverse molecular, morphological and spatial distributions.
Existing approaches to assess metabolic functions of tumors generally do so at tissue-scale
resolution. As such, the cellular basis for signals of metabolic heterogeneity in tumors has not
been completely elucidated.
This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient
treatment.
Histology
Histology is the study of the microscopic anatomy of cells and tissues of organisms. Histological
analysis is performed by examining a thin slice (section) of tissue under a light (optical) or electron
microscope.
In histology image analysis for cancer diagnosis, histopathologists visually examine the
regularities of cell shapes and tissue distributions, decide whether tissue regions are cancerous,
and determine the malignancy level.
Limitations of histology
The use of histology, molecular, and imaging data is currently the gold standard for the clinical
diagnosis of cancers and identification of prognostic and therapeutic targets. Tissue evaluation of
stained or labeled slides is traditionally performed manually by highly trained histopathologists. In
recent years, computer-assisted diagnosis (CAD) systems have been implemented to aid
histopathologists and clinicians in cancer diagnosis and research, which significantly reduce labor
requirements and subjectivity in analysis. However, H&E stained images only provide tissue
morphology information and may not resolve healthy cells from early-stage cancerous cells when
changes may be purely biochemical in nature.
Accuracy of histology delineation using traditional approaches can be problematic. For example,
delineation of pancreatectomy margins can be variable and subjective, with false-negative results
occurring in up to 20% to 30% of pancreatic adenocarcinoma patients, usually leading to additional
surgical intervention. Hence, there is a requirement for more specific biochemical markers that can
be used to accurately delineate tumor boundaries preventing the potential for tumor re-emergence
and minimizing complications caused by excessive removal of healthy tissue.
Biomarkers are “a characteristic that is objectively measured and evaluated as an indicator of normal
biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
In clinical laboratories, imaging biomarkers are utilized for disease diagnosis and grading and
evaluating therapeutic effect and/or toxicity. In research settings, biomarkers are used in
pathogenesis and drug discovery and studies to investigate pathophysiological effect and efficacy.
Traditional imaging methodologies such as immunohistochemical labeling or staining combined with
fluorescence microscopy allow for the visualization of tumor biomarkers and tissue structures with
high specificity and at high spatial resolution. However, these methods are targeted, requiring prior
,knowledge about the analyte (lose important spatial information on the analyte) to be studied, and
are limited by the small number of molecules that can be visualized simultaneously.
Mass spectrometry
Mass spectrometry can generate profiles that contain hundreds of biomolecules directly from tissue.
Spatially correlated mass spectrometry, imaging MS, reveals how each biomolecular ion varies across
tissue samples. By combining imaging MS with histology, MS profiles of specific pathological entities
have been used to identify biomarkers of disease, and when combined with clinical outcomes identify
signatures associated with prognosis and response to therapy.
Using mass spectrum, you can visualize where the compounds are present in the sample
The more scale towards red, the higher the concentration of the compound
Have information of what is in the tissue and where it is in the tissue
Mass spectrometry to measure lipids in tumor tissue
Due to the lack of methods to examine lipid content, trafficking, and distribution, several techniques,
including mass spectrometry, have become essential for a comprehensive examination of the
distribution, functions, and consequences of lipid variation in the context of disease. Mass
spectrometry imaging has rapidly risen to become a premier technology for lipid imaging in biological
tissues and cells owing to its high specificity, sensitivity, and offered increasing spatial capabilities.
, Mass spectrometry imaging is a valuable tool for the lipidomic analysis of cancer tissue as it adds
the specificity of MS to detailed spatial information.
MSI relies on the desorption of molecules present on the surface of a solid flat sample.
In MSI, not only are intensities of mass to charge (m/z) values recorded but also the respective
positions within the sample allowing for the generation of images representing ion intensities at
specific tissue localizations.
This information is of particular interest when analytes such as lipids are not homogeneously
distributed but compartmentalized in tissue.
Mass spectrometry is required to determine the exact fatty acid composition of the lipid. The
fatty acid composition of lipids in tumors has been shown to differ substantially from surrounding
healthy tissue, and fatty acid metabolism has been shown to correlate with malignant phenotypes
including metastasis, therapeutic resistance and relapse.
Staining vs. MSI Workflow
Typical mass spectrometry workflow
, Biopsy specimens are collected, snap frozen, and sectioned onto compatible slides using a cryostat. MS images are acquired by rastering the
ionizing beam across the tissue surface. Ion distribution maps of lipids are reconstructed using software. Multivariate statistical analysis is
performed to identify candidate biomarker lipids or lipid signatures of tumors. Histological staining is performed on the sam e or adjacent
tissue section. MS imaging dataset is coregistered with optical image and candidate biomarkers are correlated with histologically defined
regions or cell populations. Potential biomarkers have multiple applications in cancer research.
A typical MSI experiment is performed by rastering an ionization beam across a tissue surface at
defined x,y coordinates, thereby desorbing/ionizing analytes that are subsequently extracted into the
mass spectrometer for analysis. Software is used to reconstruct an image from the dataset in which
each pixel consists of a mass spectrum. Images can be plotted for any selected peak in the acquired
mass spectrum with the relative abundances displayed as a false-color image across the analyzed
region of interest.
Normalization is critically important for the proper interpretation of MSI datasets. Normalization is
the process of multiplying a mass spectrum with an intensity-scaling factor to expand or reduce the
range of the intensity axis. It is used to remove instrumental and systemic artifacts that impact signal
intensities by projecting spectra of varying intensity onto a common intensity scale.Artifacts may
originate from matrix application, matrix crystal heterogeneity, or ion source contamination.
Ionizing Modalities
The three most commonly used MSI ion sources are MALDI MS (matrix-assisted laser
desorption/ionization mass spectrometry), DESI-MS (desorption electrospray ionization mass
spectrometry), and SIMS (secondary ion mass spectrometry).
