microbiome signatures definitions

Inductively Coupled Plasma Mass Spectrometry (ICP-MS) is capable of detecting and quantifying elements at parts-per-trillion (ppt) levels. The technique links environmental, clinical, and molecular microbiology by revealing how essential and toxic metals shape community structure and function.

Inductively Coupled Plasma Mass Spectrometry (ICP-MS)

Researched by:

  • Karen Pendergrass ID
    Karen Pendergrass

    User avatarKaren Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.

August 1, 2025

Inductively Coupled Plasma Mass Spectrometry (CP-S) is an analytical technique used to determine the elemental composition of a sample by ionizing the sample with an inductively coupled plasma and then measuring the mass-to-charge ratio of the ions. CP-S is a highly sensitive method, capable of detecting elements at trace and ultra-trace levels, making it valuable in […]

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Researched by:

  • Karen Pendergrass ID
    Karen Pendergrass

    User avatarKaren Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.

Last Updated: August 1, 2025

Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.

Karen Pendergrass

Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.

Overview

Inductively Coupled Plasma Mass Spectrometry (ICP-MS) is an analytical technique used to determine the elemental composition of a sample by ionizing the sample with an inductively coupled plasma and then measuring the mass-to-charge ratio of the ions. ICP-MS is a highly sensitive method, capable of detecting elements at trace and ultra-trace levels, making it valuable in various fields like environmental monitoring, geochemistry, materials science, and metallomic signature characterizations for various conditions, including Alzheimer’s dementia, and Parkinson’s disease.

Detection

ICP-MS is known for detecting very low concentrations of elements, often at the parts-per-trillion (ppt) level or even parts-per-quadrillion (ppq). It can simultaneously detect multiple elements in a single measurement. ICP-MS can also be used to determine the isotopic composition of elements, which can be valuable in various applications, including geochemistry and environmental studies. 

Analytical purpose of ICP-MS in microbiome studies

Inductively Coupled Plasma Mass Spectrometry (ICP-MS) offers quantitative, multi-element and isotopic profiling of toxic metals, metalloids and selected non-metal elements in complex biological matrices (faeces, soils, culture broths, host tissues). Applications include:


ApplicationExample outputsRelevance
Metallome characterisationµg kg⁻¹ dry-weight concentrations of >70 elementsNutrient limitation, metal-driven community shifts
Heavy-metal exposureCd, Pb, Hg, As at ng g⁻¹ levelsEcotoxicology, urban microbiome surveillance
Stable-isotope tracing⁵⁷Fe / ⁵⁶Fe, ⁷⁰Zn / ⁶⁴Zn ratiosNutrient flux, trophic interactions

Research Feed

Metallomic signatures of brain tissues distinguishes between cases of dementia with Lewy bodies, Alzheimer’s disease, and Parkinson’s disease dementia
June 26, 2024
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Metallomic Signatures
Metallomic Signatures

Did you know?
Metallomic signatures can reveal hidden drivers of disease by mapping how trace metals like nickel, iron, and cadmium shape microbial behavior and immune responses. These signatures not only help identify toxic exposures but also spotlight metal-dependent pathogens, offering new targets for precision-guided therapies.

Dementia with Lewy bodies (DLB) brains show widespread copper depletion and region-specific sodium, manganese, iron, and selenium alterations. While copper loss is common to AD and PDD, DLB presents a distinct metallomic fingerprint, enabling disease differentiation via PCA. Metallomic profiling may aid in diagnosing overlapping dementias and reveals unique pathophysiological signatures.

What was studied?

This original research study investigated whether the metallomic signature of dementia with Lewy bodies (DLB) differs from Alzheimer’s disease (AD) and Parkinson’s disease dementia (PDD). The study sought to determine if post-mortem changes in elemental concentrations—particularly in essential metals—could help differentiate these often-overlapping neurodegenerative conditions. Using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), the authors quantified concentrations of nine elements (Na, Mg, K, Ca, Mn, Fe, Cu, Zn, and Se) across 10 brain regions from DLB patients and age-/sex-matched controls. These findings were directly compared to previously published metallomic profiles for AD and PDD, produced using identical methodologies. Multivariate analyses (PCA and PLS-DA) were employed to assess the potential for disease discrimination based on metal signatures.

Who was studied?

The study analyzed post-mortem brain tissue from 23 DLB patients and 20 controls, collected across ten distinct brain regions. Comparative analyses included prior datasets from similarly matched AD and PDD patient cohorts.

What were the most important findings?

In this study, region-specific metallomic signatures profiling revealed distinct trace element alterations in Dementia with Lewy Bodies (DLB). Copper (Cu) levels were consistently decreased in five of ten DLB brain regions, including the cingulate gyrus (CG), middle temporal gyrus (MTG), primary visual cortex (PVC), substantia nigra (SN), and putamen (PUT), suggesting a widespread Cu deficiency. Sodium (Na) was elevated in four regions—medulla (MED), cerebellum (CB), MTG, and CG—while more localized changes were observed for other metals. Iron (Fe) levels were increased in the motor cortex (MCX) and CG, whereas manganese (Mn) was decreased in both the PVC and MED. Calcium (Ca) was specifically reduced in the hippocampus, and selenium (Se) was also decreased in the PVC.

No significant differences in magnesium, potassium, or zinc levels were observed between DLB and control brains. Multivariate analyses, including Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), demonstrated that DLB could be distinctly separated from Alzheimer’s disease (AD) and Parkinson’s disease dementia (PDD) based on metallomic signatures. Specifically, CG, MTG, and PVC profiles enabled discrimination between DLB and AD, while the PVC alone differentiated DLB from PDD. Notably, copper depletion emerged as the only common alteration across DLB, AD, and PDD, underscoring its potential central role in the pathogenesis of neurodegenerative diseases. The authors propose that these metallomic signatures in dementia may reflect disease-specific mechanisms, including variations in oxidative stress, protein aggregation, and mitochondrial dysfunction.

What are the greatest implications of this study?

This study provides compelling evidence that distinct metallomic signatures exist across DLB, AD, and PDD, despite shared pathology such as copper depletion. It strengthens the emerging concept that trace metal dysregulation is disease-specific, rather than a general byproduct of neurodegeneration. The findings support the idea that metallomic signatures profiling—potentially via cerebrospinal fluid or advanced imaging in living patients—could improve differential diagnosis of dementia with overlapping clinical features. Furthermore, the study reinforces the hypothesis that metal dyshomeostasis, particularly copper depletion, may be a contributing pathogenic mechanism, impairing antioxidant defenses and mitochondrial function. These findings could inform new diagnostic tools and therapeutic targets.

Metallomic Signatures

A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.

Metallomic Signatures

A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.

Metallomic Signatures

A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.

Metallomic Signatures

A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.

Metallomic Signatures

A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.

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