Automated identification of subcellular organelles by coherent anti-stokes Raman scattering

Non-invasive monitoring of microbial triterpenoid production using nonlinear microscopy techniques

Introduction: Bioproduction of plant-derived triterpenoids in recombinant microbes is receiving great attention to make these biologically active compounds industrially accessible as nutraceuticals, pharmaceutics, and cosmetic ingredients. So far, there is no direct method for detecting triterpenoids under physiological conditions on a cellular level, information yet highly relevant to rationalizing microbial engineering. Methods: Here, we show in a proof-of-concept study, that triterpenoids can be detected and monitored in living yeast cells by combining coherent anti-Stokes Raman scattering (CARS) and second-harmonic-generation (SHG) microscopy techniques. We applied CARS and SHG microscopy measurements, and for comparison classical Nile Red staining, on immobilized and growing triterpenoid-producing, and non-producing reference Saccharomyces cerevisiae strains. Results and Discussion: We found that the SHG signal in triterpenoid-producing strains is significantly higher than in a non-producing reference strain, correlating with lipophile content as determined by Nile red staining. In growing cultures, both CARS and SHG signals showed changes over time, enabling new insights into the dynamics of triterpenoid production and storage inside cells.

Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis

Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes.

CARS Imaging Advances Early Diagnosis of Cardiac Manifestation of Fabry Disease

Vibrational spectroscopy can detect characteristic biomolecular signatures and thus has the potential to support diagnostics. Fabry disease (FD) is a lipid disorder disease that leads to accumulations of globotriaosylceramide in different organs, including the heart, which is particularly critical for the patient’s prognosis. Effective treatment options are available if initiated at early disease stages, but many patients are late- or under-diagnosed. Since Coherent anti-Stokes Raman (CARS) imaging has a high sensitivity for lipid/protein shifts, we applied CARS as a diagnostic tool to assess cardiac FD manifestation in an FD mouse model. CARS measurements combined with multivariate data analysis, including image preprocessing followed by image clustering and data-driven modeling, allowed for differentiation between FD and control groups. Indeed, CARS identified shifts of lipid/protein content between the two groups in cardiac tissue visually and by subsequent automated bioinformatic discrimination with a mean sensitivity of 90–96%. Of note, this genotype differentiation was successful at a very early time point during disease development when only kidneys are visibly affected by globotriaosylceramide depositions. Altogether, the sensitivity of CARS combined with multivariate analysis allows reliable diagnostic support of early FD organ manifestation and may thus improve diagnosis, prognosis, and possibly therapeutic monitoring of FD.

Raman Research on Bleomycin-Induced DNA Strand Breaks and Repair Processes in Living Cells

Even several thousands of DNA lesions are induced in one cell within one day. DNA damage may lead to mutations, formation of chromosomal aberrations, or cellular death. A particularly cytotoxic type of DNA damage is single- and double-strand breaks (SSBs and DSBs, respectively). In this work, we followed DNA conformational transitions induced by the disruption of DNA backbone. Conformational changes of chromatin in living cells were induced by a bleomycin (BLM), an anticancer drug, which generates SSBs and DSBs. Raman micro-spectroscopy enabled to observe chemical changes at the level of single cell and to collect hyperspectral images of molecular structure and composition with sub-micrometer resolution. We applied multivariate data analysis methods to extract key information from registered data, particularly to probe DNA conformational changes. Applied methodology enabled to track conformational transition from B-DNA to A-DNA upon cellular response to BLM treatment. Additionally, increased expression of proteins within the cell nucleus resulting from the activation of repair processes was demonstrated. The ongoing DNA repair process under the BLM action was also confirmed with confocal laser scanning fluorescent microscopy.

Complex cellular environments imaged by SERS nanoprobes using sugars as an all-in-one vector

Raman spectroscopy coupled with confocal microscopy offers an alternative bioimaging technique overcoming limitations associated with sensitivity, tissue penetration and image resolution. Allied to the surface-enhanced Raman scattering (SERS) properties of gold nanoparticles (AuNP), we designed SERS nanoprobes with enhanced properties and straightforward application as bio-labelling agents for gliomas. The ensuing nanoprobes coated with simple sugar units (galactose or glucose) allowed assessing information about their intracellular localization (vesicular structures), with impressive sensitivity towards complex environments and proved the ability to overcome biological auto-fluorescence and high penetration in tissues. We validate the use of sugars as an all-in-one vector (Raman reporter, conferring high stability, biocompatibility and affinity to glioma cells) as imaging agents using an impressive technique.

The subcellular arrangement of alpha-synuclein proteoforms in the Parkinson's disease brain as revealed by multicolor STED microscopy

Various post-translationally modified (PTM) proteoforms of alpha-synuclein (aSyn)-including C-terminally truncated (CTT) and Serine 129 phosphorylated (Ser129-p) aSyn-accumulate in Lewy bodies (LBs) in different regions of the Parkinson's disease (PD) brain. Insight into the distribution of these proteoforms within LBs and subcellular compartments may aid in understanding the orchestration of Lewy pathology in PD. We applied epitope-specific antibodies against CTT and Ser129-p aSyn proteoforms and different aSyn domains in immunohistochemical multiple labelings on post-mortem brain tissue from PD patients and non-neurological, aged controls, which were scanned using high-resolution 3D multicolor confocal and stimulated emission depletion (STED) microscopy. Our multiple labeling setup highlighted a consistent onion skin-type 3D architecture in mature nigral LBs in which an intricate and structured-appearing framework of Ser129-p aSyn and cytoskeletal elements encapsulates a core enriched in CTT aSyn species. By label-free CARS microscopy we found that enrichments of proteins and lipids were mainly localized to the central portion of nigral aSyn-immunopositive (aSyn+) inclusions. Outside LBs, we observed that 122CTT aSyn+ punctae localized at mitochondrial membranes in the cytoplasm of neurons in PD and control brains, suggesting a physiological role for 122CTT aSyn outside of LBs. In contrast, very limited to no Ser129-p aSyn immunoreactivity was observed in brains of non-neurological controls, while the alignment of Ser129-p aSyn in a neuronal cytoplasmic network was characteristic for brains with (incidental) LB disease. Interestingly, Ser129-p aSyn+ network profiles were not only observed in neurons containing LBs but also in neurons without LBs particularly in donors at early disease stage, pointing towards a possible subcellular pathological phenotype preceding LB formation. Together, our high-resolution and 3D multicolor microscopy observations in the post-mortem human brain provide insights into potential mechanisms underlying a regulated LB morphogenesis.

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.

Raman Imaging: An Impending Approach Towards Cancer Diagnosis

In accordance with the recent studies, Raman spectroscopy is well experimented as a highly sensitive analytical and imaging technique in biomedical research, mainly for various disease diagnosis including cancer. In comparison with other imaging modalities, Raman spectroscopy facilitate numerous assistances owing to its low background signal, immense spatial resolution, high chemical specificity, multiplexing capability, excellent photo stability and non-invasive detection capability. In cancer diagnosis Raman imaging intervened as a promising investigative tool to provide molecular level information to differentiate the cancerous vs non-cancerous cells, tissues and even in body fluids. Anciently, spontaneous Raman scattering is very feeble due to its low signal intensity and long acquisition time but new advanced techniques like coherent Raman scattering (CRS) and surface enhanced Raman scattering (SERS) gradually superseded these issues. So, the present review focuses on the recent developments and applications of Raman spectroscopy-based imaging techniques for cancer diagnosis.

Molecular contrast on phase-contrast microscope

An optical microscope enables image-based findings and diagnosis on microscopic targets, which is indispensable in many scientific, industrial and medical settings. A standard benchtop microscope platform, equipped with e.g., bright-field and phase-contrast modes, is of importance and convenience for various users because the wide-field and label-free properties allow for morphological imaging without the need for specific sample preparation. However, these microscopes never have capability of acquiring molecular contrast in a label-free manner. Here, we develop a simple add-on optical unit, comprising of an amplitude-modulated mid-infrared semiconductor laser, that is attached to a standard microscope platform to deliver the additional molecular contrast of the specimen on top of its conventional microscopic image, based on the principle of photothermal effect. We attach this unit, termed molecular-contrast unit, to a standard phase-contrast microscope, and demonstrate high-speed label-free molecular-contrast phase-contrast imaging of silica-polystyrene microbeads mixture and molecular-vibrational spectroscopic imaging of HeLa cells. Our simple molecular-contrast unit can empower existing standard microscopes and deliver a convenient accessibility to the molecular world.

Lewy pathology in Parkinson's disease consists of crowded organelles and lipid membranes

Parkinson's disease, the most common age-related movement disorder, is a progressive neurodegenerative disease with unclear etiology. Key neuropathological hallmarks are Lewy bodies and Lewy neurites: neuronal inclusions immunopositive for the protein α-synuclein. In-depth ultrastructural analysis of Lewy pathology is crucial to understanding pathogenesis of this disease. Using correlative light and electron microscopy and tomography on postmortem human brain tissue from Parkinson's disease brain donors, we identified α-synuclein immunopositive Lewy pathology and show a crowded environment of membranes therein, including vesicular structures and dysmorphic organelles. Filaments interspersed between the membranes and organelles were identifiable in many but not all α-synuclein inclusions. Crowding of organellar components was confirmed by stimulated emission depletion (STED)-based super-resolution microscopy, and high lipid content within α-synuclein immunopositive inclusions was corroborated by confocal imaging, Fourier-transform coherent anti-Stokes Raman scattering infrared imaging and lipidomics. Applying such correlative high-resolution imaging and biophysical approaches, we discovered an aggregated protein-lipid compartmentalization not previously described in the Parkinsons' disease brain.

Detecting and Quantifying Microscale Chemical Reactions in Pharmaceutical Tablets by Stimulated Raman Scattering Microscopy

It has been estimated that approximately 50% of all marketed drug molecules are manufactured and administered in the form of salts, often with the goal of improving solubility, dissolution rate, and efficacy of the drug. However, salt disproportionation during processing or storage is a common adverse effect in these formulations. Due to the heterogeneous nature of solid drug formulations, it is essential to characterize the drug substances noninvasively at micrometer resolution to understand the molecular mechanism of salt disproportionation. However, there is a lack of such capability with current characterization methods. In this study, we demonstrate that stimulated Raman scattering (SRS) microscopy can be used to provide sensitive and quantitative chemical imaging of the salt disproportionation reaction of pioglitazone hydrochloride (PIO-HCl) at a very low drug loading (1% w/w). Our findings illuminate a water mediated pathway of drug disproportionation and highlight the importance of noninvasive chemical imaging in a mechanistic study of solid-state chemical reactions.

Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm^-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

Chemical fingerprinting of single glandular trichomes of Cannabis sativa by Coherent anti-Stokes Raman scattering (CARS) microscopy

BACKGROUND

Cannabis possesses a rich spectrum of phytochemicals i.e. cannabinoids, terpenes and phenolic compounds of industrial and medicinal interests. Most of these high-value plant products are synthesised in the disk cells and stored in the secretory cavity in glandular trichomes. Conventional trichome analysis was so far based on optical microscopy, electron microscopy or extraction based methods that are either limited to spatial or chemical information. Here we combine both information to obtain the spatial distribution of distinct secondary metabolites on a single-trichome level by applying Coherent anti-Stokes Raman scattering (CARS), a microspectroscopic technique, to trichomes derived from sepals of a drug- and a fibre-type.

RESULTS

Hyperspectral CARS imaging in combination with a nonlinear unmixing method allows to identify and localise Δ⁹-tetrahydrocannabinolic acid (THCA) in the secretory cavity of drug-type trichomes and cannabidiolic acid (CBDA)/myrcene in the secretory cavity of fibre-type trichomes, thus enabling an easy discrimination between high-THCA and high-CBDA producers. A unique spectral fingerprint is found in the disk cells of drug-type trichomes, which is most similar to cannabigerolic acid (CBGA) and is not found in fibre-type trichomes. Furthermore, we differentiate between different cell types by a combination of CARS with simultaneously acquired two-photon fluorescence (TPF) of chlorophyll a from chloroplasts and organic fluorescence mainly arising from cell walls enabling 3D visualisation of the essential oil distribution and cellular structures.

CONCLUSION

Here we demonstrate a label-free and non-destructive method to analyse the distribution of secondary metabolites and distinguish between different cell and chemo-types with high spatial resolution on a single trichome. The record of chemical fingerprints of single trichomes offers the possibility to optimise growth conditions as well as guarantee a direct process control for industrially cultivated medicinal Cannabis plants. Moreover, this method is not limited to Cannabis related issues but can be widely implemented for optimising and monitoring all kinds of natural or biotechnological production processes with simultaneous spatial and chemical information.

Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level

Monitoring the drug efficacy or resistance in vitro is usually carried out by measuring the response of single few proteins. However, observation of single proteins instead of an integral cell response may lead to results that are not consistent with patient’s response to a drug. We present a Raman spectroscopic method that detects the integral cell response to drugs such as tyrosine kinase inhibitors (TKIs). Non-small cell lung cancer (NSCLC) patients with EGFR mutations develop acquired resistance to first (erlotinib)- and third (osimertinib)-generation TKIs. Large erlotinib-induced differences were detected by Raman micro-spectroscopy in NSCLC cells without T790M EGFR mutation but not in cells with this mutation. Additionally, Raman difference spectra detected the response of NSCLC cells with T790M EGFR mutation to second- (neratinib) and third-generation (osimertinib) TKIs, and the resistance of cells with T790M/C797S EGFR mutation to osimertinib. Thus, the in vitro Raman results indicated that NSCLC cells with T790M and T790M/C797S EGFR mutations are resistant to erlotinib- and osimertinib, respectively, consistent with the observed responses of patients. This study shows the potential of Raman micro-spectroscopy to monitor drug resistance and opens a new door to in vitro companion diagnostics for screening personalized therapies.

Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology

Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-microscopy-based cytopathology. Conceptually, DCNNs facilitate a flexible combination of spectral and spatial information for classifying cellular images as healthy or cancer-affected cells. As we demonstrate, this conceptual advantage translates into practice, where DCNNs exceed the accuracy of both conventional classifiers based on pixel spectra as well as classifiers based on morphological features extracted from Raman microscopic images. Remarkably, accuracies exceeding those of all previously proposed classifiers are obtained while using only a small fraction of the spectral information provided by the dataset. Overall, our results indicate a high potential for DCNNs in medical applications of not just Raman, but also infrared microscopy.

Raman Microspectroscopic Evidence for the Metabolism of a Tyrosine Kinase Inhibitor, Neratinib, in Cancer Cells

Tyrosine kinase receptors are one of the main targets in cancer therapy. They play an essential role in the modulation of growth factor signaling and thereby inducing cell proliferation and growth. Tyrosine kinase inhibitors such as neratinib bind to EGFR and HER2 receptors and exhibit antitumor activity. However, little is known about their detailed cellular uptake and metabolism. Here, we report for the first time the intracellular spatial distribution and metabolism of neratinib in different cancer cells using label-free Raman imaging. Two new neratinib metabolites were detected and fluorescence imaging of the same cells indicate that neratinib accumulates in lysosomes. The results also suggest that both EGFR and HER2 follow the classical endosome lysosomal pathway for degradation. A combination of Raman microscopy, DFT calculations, and LC-MS was used to identify the chemical structure of neratinib metabolites. These results show the potential of Raman microscopy to study drug pharmacokinetics.

Label-free identification of myopathological features with coherent anti-Stokes Raman scattering

INTRODUCTION

The aim of this study was the label-free identification of distinct myopathological features with coherent anti-Stokes Raman scattering (CARS) imaging, which leaves the sample intact for further analysis.

METHODS

The protein distribution was determined without labels by CARS at 2,930 cm^-1 and was compared with the results of standard histological staining.

RESULTS

CARS imaging allowed the visualization of glycogen accumulation in glycogen storage disease type 5 (McArdle disease) and of internal nuclei in centronuclear myopathy. CARS identified an inhomogeneous protein distribution within muscle fibers in sporadic inclusion body myositis that was not shown with standard staining. In Duchenne muscular dystrophy, evidence for a higher protein content at the border of hypercontracted fibers was detected.

DISCUSSION

CARS enables the label-free identification of distinct myopathological features, possibly paving the way for subsequent proteomic, metabolic, and genomic analyses. Muscle Nerve 58: 457-460, 2018.

Noninvasive Diagnosis of High-Grade Urothelial Carcinoma in Urine by Raman Spectral Imaging

The current gold standard for the diagnosis of bladder cancer is cystoscopy, which is invasive and painful for patients. Therefore, noninvasive urine cytology is usually used in the clinic as an adjunct to cystoscopy; however, it suffers from low sensitivity. Here, a novel noninvasive, label-free approach with high sensitivity for use with urine is presented. Coherent anti-Stokes Raman scattering imaging of urine sediments was used in the first step for fast preselection of urothelial cells, where high-grade urothelial cancer cells are characterized by a large nucleus-to-cytoplasm ratio. In the second step, Raman spectral imaging of urothelial cells was performed. A supervised classifier was implemented to automatically differentiate normal and cancerous urothelial cells with 100% accuracy. In addition, the Raman spectra not only indicated the morphological changes that are identified by cytology with hematoxylin and eosin staining but also provided molecular resolution through the use of specific marker bands. The respective Raman marker bands directly show a decrease in the level of glycogen and an increase in the levels of fatty acids in cancer cells as compared to controls. These results pave the way for "spectral" cytology of urine using Raman microspectroscopy.

A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum

We develop and discuss a methodology for batch-level analysis of hyperspectral stimulated Raman scattering (hsSRS) data sets of human meibum in the CH-stretching vibrational range. The analysis consists of two steps. The first step uses a training set (n=19) to determine chemically meaningful reference spectra that jointly constitute a basis set for the sample. This procedure makes use of batch-level vertex component analysis (VCA), followed by unsupervised k-means clustering to express the data set in terms of spectra that represent lipid and protein mixtures in changing proportions. The second step uses a random forest classifier to rapidly classify hsSRS stacks in terms of the pre-determined basis set. The overall procedure allows a rapid quantitative analysis of large hsSRS data sets, enabling a direct comparison among samples using a single set of reference spectra. We apply this procedure to assess 50 specimens of expressed human meibum, rich in both protein and lipid, and show that the batch-level analysis reveals marked variation among samples that potentially correlate with meibum health quality.

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Protein expression guided chemical profiling of living cells by the simultaneous observation of Raman scattering and anti-Stokes fluorescence emission

Our current understanding of molecular biology provides a clear picture of how the genome, transcriptome and proteome regulate each other, but how the chemical environment of the cell plays a role in cellular regulation remains much to be studied. Here we show an imaging method using hybrid fluorescence-Raman microscopy that measures the chemical micro-environment associated with protein expression patterns in a living cell. Simultaneous detection of fluorescence and Raman signals, realised by spectrally separating the two modes through the single photon anti-Stokes fluorescence emission of fluorescent proteins, enables the accurate correlation of the chemical fingerprint of a specimen to its physiological state. Subsequent experiments revealed the slight chemical differences that enabled the chemical profiling of mouse embryonic stem cells with and without Oct4 expression. Furthermore, using the fluorescent probe as localisation guide, we successfully analysed the detailed chemical content of cell nucleus and Golgi body. The technique can be further applied to a wide range of biomedical studies for the better understanding of chemical events during biological processes.

Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications

First, the potential role of Raman-based techniques in biomedicine is introduced. Second, an overview about the instrumentation for spontaneous and coherent Raman scattering microscopic imaging is given with a focus of recent developments. Third, imaging strategies are summarized including sequential registration with laser scanning microscopes, line imaging and global or wide-field imaging. Finally, examples of biomedical applications are presented in the context of single cells, laser tweezers, tissue sections, biopsies and whole animals.

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

Cell theory has officially reached 350 years of age as the first use of the word 'cell' in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication 'Micrographia: or some physiological definitions of minute bodies'. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the subcellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more interdisciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking.

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.

In vitro prediction of the efficacy of molecularly targeted cancer therapy by Raman spectral imaging

Mutational acquired resistance is a major challenge in cancer therapy. Somatic tumours harbouring some oncogenic mutations are characterised by a high mortality rate. Surprisingly, preclinical evaluation methods do not show clearly resistance of mutated cancers to some drugs. Here, we implemented Raman spectral imaging to investigate the oncogenic mutation resistance to epidermal growth factor receptor targeting therapy. Colon cancer cells with and without oncogenic mutations such as KRAS and BRAF mutations were treated with erlotinib, an inhibitor of epidermal growth factor receptor, in order to detect the impact of these mutations on Raman spectra of the cells. Clinical studies suggested that oncogenic KRAS and BRAF mutations inhibit the response to erlotinib therapy in patients, but this effect is not observed in vitro. The Raman results indicate that erlotinib induces large spectral changes in SW-48 cells that harbour wild-type KRAS and BRAF. These spectral changes can be used as a marker of response to therapy. HT-29 cells (BRAF mutated) and SW-480 cells (KRAS mutated) display a smaller and no significant response, respectively. However, the erlotinib effect on these cells is not observed when phosphorylation of extracellular-signal-regulated kinase and AKT is monitored by Western blot, where this phosphorylation is the conventional in vitro test. Lipid droplets show a large response to erlotinib only in the case of cells harbouring wild-type KRAS and BRAF, as indicated by Raman difference spectra. This study shows the great potential of Raman spectral imaging as an in vitro tool for detecting mutational drug resistance.

Label-Free Raman Spectroscopic Imaging Monitors the Integral Physiologically Relevant Drug Responses in Cancer Cells

Predictions about the cellular efficacy of drugs tested in vitro are usually based on the measured responses of a few proteins or signal transduction pathways. However, cellular proteins are highly coupled in networks, and observations of single proteins may not adequately reflect the in vivo cellular response to drugs. This might explain some large discrepancies between in vitro drug studies and drug responses observed in patients. We present a novel in vitro marker-free approach that enables detection of cellular responses to a drug. We use Raman spectral imaging to measure the effect of the epidermal growth factor receptor (EGFR) inhibitor panitumumab on cell lines expressing wild-type Kirsten-Ras (K-Ras) and oncogenic K-Ras mutations. Oncogenic K-Ras mutation blocks the response to anti-EGFR therapy in patients, but this effect is not readily observed in vitro. The Raman studies detect large panitumumab-induced differences in vitro in cells harboring wild-type K-Ras as seen in A in red but not in cells with K-Ras mutations as seen in B; these studies reflect the observed patient outcomes. However, the effect is not observed when extracellular-signal-regulated kinase phosphorylation is monitored. The Raman spectra show for cells with wild-type K-Ras alterations based on the responses to panitumumab. The subcellular component with the largest spectral response to panitumumab was lipid droplets, but this effect was not observed when cells harbored K-Ras mutations. This study develops a noninvasive, label-free, in vitro vibrational spectroscopic test to determine the integral physiologically relevant drug response in cell lines. This approach opens a new field of patient-centered drug testing that could deliver superior patient therapies.

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.