Deeper understanding of biological tissue: quantitative correlation of MALDI-TOF and Raman imaging

Opto-Lipidomics of Tissues

Lipid metabolism and signaling play pivotal functions in biology and disease development. Despite this, currently available optical techniques are limited in their ability to directly visualize the lipidome in tissues. In this study, opto-lipidomics, a new approach to optical molecular tissue imaging is introduced. The capability of vibrational Raman spectroscopy is expanded to identify individual lipids in complex tissue matrices through correlation with desorption electrospray ionization (DESI) - mass spectrometry (MS) imaging in an integrated instrument. A computational pipeline of inter-modality analysis is established to infer lipidomic information from optical vibrational spectra. Opto-lipidomic imaging of transient cerebral ischemia-reperfusion injury in a murine model of ischemic stroke demonstrates the visualization and identification of lipids in disease with high molecular specificity using Raman scattered light. Furthermore, opto-lipidomics in a handheld fiber-optic Raman probe is deployed and demonstrates real-time classification of bulk brain tissues based on specific lipid abundances. Opto-lipidomics opens a host of new opportunities to study lipid biomarkers for diagnostics, prognostics, and novel therapeutic targets.

Visualisation of drug distribution in skin using correlative optical spectroscopy and mass spectrometry imaging

A correlative methodology for label-free chemical imaging of soft tissue has been developed, combining non-linear optical spectroscopies and mass spectrometry to achieve sub-micron spatial resolution and critically improved drug detection sensitivity. The approach was applied to visualise the kinetics of drug reservoir formation within human skin following in vitro topical treatment with a commercial diclofenac gel. Non-destructive optical spectroscopic techniques, namely stimulated Raman scattering, second harmonic generation and two photon fluorescence microscopies, were used to provide chemical and structural contrast. The same tissue sections were subsequently analysed by secondary ion mass spectrometry, which offered higher sensitivity for diclofenac detection throughout the epidermis and dermis. A method was developed to combine the optical and mass spectrometric datasets using image registration techniques. The label-free, high-resolution visualisation of tissue structure coupled with sensitive chemical detection offers a powerful method for drug biodistribution studies in the skin that impact directly on topical pharmaceutical product development.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.

Surface-assisted laser desorption/ionization mass spectrometry imaging: A review

In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.

SALDI-MS and SERS Multimodal Imaging: One Nanostructured Substrate to Rule Them Both

Imaging techniques based on mass spectrometry or spectroscopy methods inform in situ about the chemical composition of biological tissues or organisms, but they are sometimes limited by their specificity, sensitivity, or spatial resolution. Multimodal imaging addresses these limitations by combining several imaging modalities; however, measuring the same sample with the same preparation using multiple imaging techniques is still uncommon due to the incompatibility between substrates, sample preparation protocols, and data formats. We present a multimodal imaging approach that employs a gold-coated nanostructured silicon substrate to couple surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced Raman spectroscopy (SERS). Our approach integrates both imaging modalities by using the same substrate, sample preparation, and data analysis software on the same sample, allowing the coregistration of both images. We transferred molecules from clean fingertips and fingertips covered with plasticine modeling clay onto our nanostructure and analyzed their chemical composition and distribution by SALDI-MS and SERS. Multimodal analysis located the traces of plasticine on fingermarks and provided chemical information on the composition of the clay. Our multimodal approach effectively combines the advantages of mass spectrometry and vibrational spectroscopy with the signal enhancing abilities of our nanostructured substrate.

LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects

Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within ±50 μm. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Betulinic acid and its spray dried microparticle formulation: In vitro PDT effect against ovarian carcinoma cell line and in vivo plasma and tumor disposition

The race against ovarian cancer continue to motivate the research worldwide. It is known that many antitumor drugs have limited penetration into solid tumor tissues due to its microenvironment, thus contributing to their low efficacy. Therapeutic modalities have been exploited to elicit antitumor effects based on microenvironment of tumor, including Photodynamic therapy (PDT). Prospection of natural small molecules and nanotechnology are important tools in the development of new ways of obtaining photoactive compounds that are biocompatible. The Betulinic acid (BA) has shown potential biological effect as bioactive drug, but it has low water solubility. Thus, in the present study, owing to the poor solubility of the BA, its free form (BAF) was compared to a spray dried microparticle betulinic acid/HP-β-CD formulation (BAC) aiming to assess the BAF and BAC efficacy as a photosensitizer in PDT for application in ovarian cancer. BAF and BAC were submitted to assays in the presence of LED (λ = 420 nm) under different conditions (2.75 J/cm2, 5.5 J/cm2, and 11 J/cm2) and in absence of irradiation, after 5 min or 4 h of contact with ovarian carcinoma cells (A2780) or fibroblast murine cells (3T3). Furthermore, HPLC-MS/MS and MALDI-MSI methods were developed and validated in plasma and tumor of mice proving suitable for in vivo studies. The results found a greater photoinduced cytotoxic effect for the BAC at low concentration for A2780 when irradiated with LED with similar results for fluorescence microscopy. The results motivate us to continue the studies with the BA as a potential antitumor bioactive compound.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Perspective on Multimodal Imaging Techniques Coupling Mass Spectrometry and Vibrational Spectroscopy: Picturing the Best of Both Worlds

Studies on complex biological phenomena often combine two or more imaging techniques to collect high-quality comprehensive data directly in situ, preserving the biological context. Mass spectrometry imaging (MSI) and vibrational spectroscopy imaging (VSI) complement each other in terms of spatial resolution and molecular information. In the past decade, several combinations of such multimodal strategies arose in research fields as diverse as microbiology, cancer, and forensics, overcoming many challenges toward the unification of these techniques. Here we focus on presenting the advantages and challenges of multimodal imaging from the point of view of studying biological samples as well as giving a perspective on the upcoming trends regarding this topic. The latest efforts in the field are discussed, highlighting the purpose of the technique for clinical applications.

Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format

BACKGROUND

Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand.

RESULTS

Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications.

CONCLUSION

Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

Deep learning a boon for biophotonics?

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

Raman fingerprints as promising markers of cellular senescence and aging

Due to our aging population, understanding of the underlying molecular mechanisms constantly gains more and more importance. Senescent cells, defined by being irreversibly growth arrested and associated with a specific gene expression and secretory pattern, accumulate with age and thus contribute to several age-related diseases. However, their specific detection, especially in vivo, is still a major challenge. Raman microspectroscopy is able to record biochemical fingerprints of cells and tissues, allowing a distinction between different cellular states, or between healthy and cancer tissue. Similarly, Raman microspectroscopy was already successfully used to distinguish senescent from non-senescent cells, as well as to investigate other molecular changes that occur at cell and tissue level during aging. This review is intended to give an overview about various applications of Raman microspectroscopy to study aging, especially in the context of detecting senescent cells.

Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra

The last two decades have seen tremendous progress in the application of two-dimensional correlation spectroscopy (2D-COS) as a versatile analysis method for data series obtained using a large variety of different spectroscopic modalities, including infrared (IR) and Raman spectroscopy. The analysis technique is applicable to a series of spectra recorded under the influence of an external sample perturbation. Two-dimensional COS analysis is not only helpful to decipher correlations, which may exist between distinct spectral features, but can also be utilized to obtain the sequence of individual spectral changes. The focus of this review article is on the application of 2D-COS for analyzing spatially resolved data with special emphasis on hyperspectral imaging (HSI) study. In this review, we briefly introduce the fundamentals of the generalized 2D-COS analysis approach, discuss specific points of 2D-COS application to spatially resolved spectra and demonstrate essential aspects of data pre-processing for 2D-COS analysis of spatially resolved spectra. Based on illustrative examples, we show that 2D-COS is useful for spectral band assignment in HSI applications and demonstrate its utility for detecting subtle correlations between spectra features, or between features from different imaging modalities in the case of heterospectral (multimodal) HSI. Furthermore, a short overview on existing 2D-COS software tools is provided. It is hoped that this article represents not only a useful guideline for 2D-COS analyses of spatially resolved hyperspectral data but supports also further dissemination of the 2D-COS analysis method as a whole.

Analyzing Raman spectroscopic data

This chapter is a short introduction into the data analysis pipeline, which is typically utilized to analyze Raman spectra. We empathized in the chapter that this data analysis pipeline must be tailored to the specific application of interest. Nevertheless, the tailored data analysis pipeline consists always of the same general procedures applied sequentially. The utilized procedures correct for artefacts, standardize the measured spectral data and translate the spectroscopic signals into higher level information. These computational procedures can be arranged into separate groups namely data pre-treatment, pre-processing and modeling. Thereby the pre-treatment aims to correct for non-sample-dependent artefacts, like cosmic spikes and contributions of the measurement device. The block of procedures, which needs to be applied next, is called pre-processing. This group consists of smoothing, baseline correction, normalization and dimension reduction. Thereafter, the analysis model is constructed and the performance of the models is evaluated. Every data analysis pipeline should be composed of procedures of these three groups and we describe every group in this chapter. After the description of data pre-treatment, pre-processing and modeling, we summarized trends in the analysis of Raman spectra namely model transfer approaches and data fusion. At the end of the chapter we tried to condense the whole chapter into guidelines for the analysis of Raman spectra.

Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology

Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".

Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples

Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis.

Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models.

Fourier Transform Infrared Microscopy Enables Guidance of Automated Mass Spectrometry Imaging to Predefined Tissue Morphologies

Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.

Label-free molecular imaging of the kidney

In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.

Vibrational Spectroscopy for Imaging Single Microbial Cells in Complex Biological Samples

Vibrational spectroscopy is increasingly used for the rapid and non-destructive imaging of environmental and medical samples. Both Raman and Fourier-transform infrared (FT-IR) imaging have been applied to obtain detailed information on the chemical composition of biological materials, ranging from single microbial cells to tissues. Due to its compatibility with methods such as stable isotope labeling for the monitoring of cellular activities, vibrational spectroscopy also holds considerable power as a tool in microbial ecology. Chemical imaging of undisturbed biological systems (such as live cells in their native habitats) presents unique challenges due to the physical and chemical complexity of the samples, potential for spectral interference, and frequent need for real-time measurements. This Mini Review provides a critical synthesis of recent applications of Raman and FT-IR spectroscopy for characterizing complex biological samples, with a focus on developments in single-cell imaging. We also discuss how new spectroscopic methods could be used to overcome current limitations of single-cell analyses. Given the inherent complementarity of Raman and FT-IR spectroscopic methods, we discuss how combining these approaches could enable us to obtain new insights into biological activities either in situ or under conditions that simulate selected properties of the natural environment.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Mass Spectrometry Imaging in Oncology Drug Discovery

Over the last decade mass spectrometry imaging (MSI) has been integrated in to many areas of drug discovery and development. It can have significant impact in oncology drug discovery as it allows efficacy and safety of compounds to be assessed against the backdrop of the complex tumour microenvironment. We will discuss the roles of MSI in investigating compound and metabolite biodistribution and defining pharmacokinetic -pharmacodynamic relationships, analysis that is applicable to all drug discovery projects. We will then look more specifically at how MSI can be used to understand tumour metabolism and other applications specific to oncology research. This will all be described alongside the challenges of applying MSI to industry research with increased use of metrology for MSI.

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections

Background

In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools.

Results

We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images.

Conclusions

Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0804-9) contains supplementary material, which is available to authorized users.

Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping

A new predictive imaging modality is created through the ‘fusion’ of two distinct technologies: imaging mass spectrometry (IMS) and microscopy. IMS-generated molecular maps, rich in chemical information but having coarse spatial resolution, are combined with optical microscopy maps, which have relatively low chemical specificity but high spatial information. The resulting images combine the advantages of both technologies, enabling prediction of a molecular distribution both at high spatial resolution and with high chemical specificity. Multivariate regression is used to model variables in one technology, using variables from the other technology. Several applications demonstrate the remarkable potential of image fusion: (i) ‘sharpening’ of IMS images, which uses microscopy measurements to predict ion distributions at a spatial resolution that exceeds that of measured ion images by ten times or more; (ii) prediction of ion distributions in tissue areas that were not measured by IMS; and (iii) enrichment of biological signals and attenuation of instrumental artifacts, revealing insights that are not easily extracted from either microscopy or IMS separately. Image fusion enables a new multi-modality paradigm for tissue exploration whereby mining relationships between different imaging sensors yields novel imaging modalities that combine and surpass what can be gleaned from the individual technologies alone.

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study

This paper introduces algorithms for identifying overlapping observations between Raman and fluorescence microscopic images of one and the same sample.

Imaging and Mapping of Tissue Constituents at the Single-Cell Level Using MALDI MSI and Quantitative Laser Scanning Cytometry

For nearly a century, histopathology involved the laborious morphological analyses of tissues stained with broad-spectrum dyes (i.e., eosin to label proteins). With the advent of antibody-labeling, immunostaining (fluorescein and rhodamine for fluorescent labeling) and immunohistochemistry (DAB and hematoxylin), it became possible to identify specific immunological targets in cells and tissue preparations. Technical advances, including the development of monoclonal antibody technology, led to an ever-increasing palate of dyes, both fluorescent and chromatic. This provides an incredibly rich menu of molecular entities that can be visualized and quantified in cells-giving rise to the new discipline of Molecular Pathology. We describe the evolution of two analytical techniques, cytometry and mass spectrometry, which complement histopathological visual analysis by providing automated, cellular-resolution constituent maps. For the first time, laser scanning cytometry (LSC) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) are combined for the analysis of tissue sections. The utility of the marriage of these techniques is demonstrated by analyzing mouse brains with neuron-specific, genetically encoded, fluorescent proteins. We present a workflow that: (1) can be used with or without expensive matrix deposition methods, (2) uses LSC images to reveal the diverse landscape of neural tissue as well as the matrix, and (3) uses a tissue fixation method compatible with a DNA stain. The proposed workflow can be adapted for a variety of sample preparation and matrix deposition methods.

MALDI mass spectrometric imaging meets “omics”: recent advances in the fruitful marriage

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a method that allows the investigation of the molecular content of surfaces, in particular, tissues, within its morphological context.

Correlated imaging with C60-SIMS and confocal Raman microscopy: Visualization of cell-scale molecular distributions in bacterial biofilms

Secondary ion mass spectrometry (SIMS) and confocal Raman microscopy (CRM) are combined to analyze the chemical composition of cultured Pseudomonas aeruginosa biofilms, providing complementary chemical information for multiple analytes within the sample. Precise spatial correlation between SIMS and CRM images is achieved by applying a chemical microdroplet array to the sample surface which is used to navigate the sample, relocate regions of interest, and align image data. CRM is then employed to nondestructively detect broad molecular constituent classes-including proteins, carbohydrates, and, for the first time, quinolone signaling molecules-in Pseudomonas-derived biofilms. Subsequent SIMS imaging at the same location detects quinolone distributions in excellent agreement with the CRM, discerns multiple quinolone species which differ slightly in mass, resolves subtle differences in their distributions, and resolves ambiguous compound assignments from CRM by determining specific molecular identities via in situ tandem MS.