High resolution Raman spectroscopy mapping of stem cell micropatterns

Erlach, Thomas C. von; Hedegaard, Martin A.B.; Stevens, Molly M.

doi:10.1039/c4an02346c

Cited by 16 publications

(14 citation statements)

References 34 publications

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“…Algorithms belonging to this family of techniques include Vertex Component Analysis and N‐FINDR . Both techniques have previously been applied to Raman images . Briefly, the N‐FINDR algorithm searches for the spectral vectors spanning the largest volume of the respective image dataset.…”

Section: Methods For Multi‐image Analysismentioning

confidence: 99%

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Quantitative multi‐image analysis for biomedical Raman spectroscopic imaging

Hedegaard

Bergholt

Stevens

2016

Journal of Biophotonics

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Imaging by Raman spectroscopy enables unparalleled label-free insights into cell and tissue composition at the molecular level. With established approaches limited to single image analysis, there are currently no general guidelines or consensus on how to quantify biochemical components across multiple Raman images. Here, we describe a broadly applicable methodology for the combination of multiple Raman images into a single image for analysis. This is achieved by removing image specific background interference, unfolding the series of Raman images into a single dataset, and normalisation of each Raman spectrum to render comparable Raman images. Multivariate image analysis is finally applied to derive the contributing 'pure' biochemical spectra for relative quantification. We present our methodology using four independently measured Raman images of control cells and four images of cells treated with strontium ions from substituted bioactive glass. We show that the relative biochemical distribution per area of the cells can be quantified. In addition, using k-means clustering, we are able to discriminate between the two cell types over multiple Raman images. This study shows a streamlined quantitative multi-image analysis tool for improving cell/tissue characterisation and opens new avenues in biomedical Raman spectroscopic imaging.

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Section: Methods For Multi‐image Analysismentioning

confidence: 99%

“…specific structures of proteins, lipids, and nucleic acids) with submicron spatial resolution that Raman spectroscopic imaging reveals. Recently, Raman spectroscopic imaging has also been used in regenerative medicine .…”

Section: Introductionmentioning

confidence: 99%

Quantitative multi‐image analysis for biomedical Raman spectroscopic imaging

Hedegaard

Bergholt

Stevens

2016

Journal of Biophotonics

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“…with high resolution and specificity [24]. Raman spectroscopy has been used to track adipogenesis [8,21,[25][26][27], osteogenesis [8,[28][29][30][31], and other stem cell linages [24,[32][33][34][35][36][37]. However, precise identification of changes in the chemical cues during stem cell differentiation from the Raman spectra obtained from the cellular components (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Label-free Monitoring of Adipogenic Stem Cell Differentiation using Endogenous Optical Biomarkers

Mehta

Shaik

Prasad

et al. 2020

Preprint

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Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective therapy requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (< 0.001% of the number of cells). Here, we report a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping (SAM)-based machine learning analysis to distinguish differentiating human adipose derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enabled identification of differentiated cells at single cell resolution. The label-free HSI method was compared with the standard methods such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. Further, we performed Raman microscopy and multiphoton-based metabolic imaging to provide complimentary information for the functional imaging of the hASCs. Finally, the HSI method was validated using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution. This could provide a powerful tool to assess the safety and efficacy of stem cell-based regenerative therapies.

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“…Gas phase porosity measurements and solute exclusion are suitable techniques for the determination of the accessible inner surface and pore size distribution, but do not provide any information on material penetration and the spatial distribution of the pores. Recently, Raman hyperspectral mapping was proposed for label-free analysis of the cellular composition and a spatial reconstruction based on chemical information (von Erlach et al, 2015 ; Hedegaard et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Tracking of Short Distance Transport Pathways in Biological Tissues by Ultra-Small Nanoparticles

et al. 2018

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In this work, ultra-small europium-doped HfO2 nanoparticles were infiltrated into native wood and used as trackers for studying penetrability and diffusion pathways in the hierarchical wood structure. The high electron density, laser induced luminescence, and crystallinity of these particles allowed for a complementary detection of the particles in the cellular tissue. Confocal Raman microscopy and high-resolution synchrotron scanning wide-angle X-ray scattering (WAXS) measurements were used to detect the infiltrated particles in the native wood cell walls. This approach allows for simultaneously obtaining chemical information of the probed biological tissue and the spatial distribution of the integrated particles. The in-depth information about particle distribution in the complex wood structure can be used for revealing transport pathways in plant tissues, but also for gaining better understanding of modification treatments of plant scaffolds aiming at novel functionalized materials.

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High resolution Raman spectroscopy mapping of stem cell micropatterns

Cited by 16 publications

References 34 publications

Quantitative multi‐image analysis for biomedical Raman spectroscopic imaging

Quantitative multi‐image analysis for biomedical Raman spectroscopic imaging

Multimodal Label-free Monitoring of Adipogenic Stem Cell Differentiation using Endogenous Optical Biomarkers

Tracking of Short Distance Transport Pathways in Biological Tissues by Ultra-Small Nanoparticles

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