Fluorescence saturation imaging microscopy: molecular fingerprinting with a standard confocal microscope

Molecular specificity in fluorescence imaging of cells and tissues can be increased by measuring parameters other than intensity. For instance, fluorescence lifetime imaging became a widespread modality for biomedical optics. Previously, we suggested using the fluorescence saturation effect at pulsed laser excitation to map the absorption cross-section as an additional molecular contrast in two-photon microscopy [Opt. Lett.47(17), 4455 (2022).10.1364/OL.465605]. Here, it is shown that, somewhat counterintuitive, fluorescence saturation can be observed under cw excitation in a standard confocal microscopy setup. Mapping the fluorescence saturation parameter allows obtaining additional information about the fluorophores in the system, as demonstrated by the example of peptide hydrogel, stained cells and unstained thyroid gland. The suggested technique does not require additional equipment and can be implemented on confocal systems as is.

Quantitative image analysis of intracellular protein translocation in 3-dimensional tissues for pharmacodynamic studies of immunogenic cell death

A recent development in cancer chemotherapy is to use cytotoxics to induce tumor-specific immune response through immunogenic cell death (ICD). In ICD, calreticulin is translocated from endoplasmic reticulum to cell membrane (ecto-CRT) which serves as the 'eat-me-signal' to antigen-presenting cells. Ecto-CRT measurements, e.g., by ecto-CRT immunostaining plus flow cytometry, can be used to study the pharmacodynamics of ICD in single cells, whereas ICD studies in intact 3-dimensional tissues such as human tumors require different approaches. The present study described a method that used (a) immunostaining with fluorescent antibodies followed by confocal microscopy to obtain the spatial locations of two molecules-of-interest (CRT and a marker protein WGA), and (b) machine-learning (trainable WEKA segmentation) and additional image processing tools to locate the target molecules, remove the interfering signals in the nucleus, cytosol and extracellular space, enable the distinction of the inner and outer edges of the cell membrane and thereby identify the cells with ecto-CRT. This method, when applied to 3-dimensional human bladder cancer cell spheroids, yielded drug-induced ecto-CRT measurements that were qualitatively comparable to the flow cytometry results obtained with single cells disaggregated from spheroids. This new method was applied to study drug-induced ICD in short-term cultures of surgical specimens of human patient bladder tumors.

Organelle specific simultaneous Raman/green fluorescence protein microspectroscopy for living cell physicochemical studies

We demonstrate a novel bio-spectroscopic technique, "simultaneous Raman/GFP microspectroscopy". It enables organelle specific Raman microspectroscopy of living cells. Fission yeast, Schizosaccharomyces pombe, whose mitochondria are green fluorescence protein (GFP) labeled, is used as a test model system. Raman excitation laser and GFP excitation light irradiate the sample yeast cells simultaneously. GFP signal is monitored in the anti-Stokes region where interference from Raman scattering is negligibly small. Of note, 13 568 Raman spectra measured from different points of 19 living yeast cells are categorized according to their GFP fluorescence intensities, with the use of a two-component multivariate curve resolution with alternate least squares (MCR-ALS) analysis in the anti-Stokes region. This categorization allows us to know whether or not Raman spectra are taken from mitochondria. Raman spectra specific to mitochondria are obtained by an MCR-ALS analysis in the Stokes region of 1389 strongly GFP positive spectra. Two mitochondria specific Raman spectra have been obtained. The first one is dominated by protein Raman bands and the second by lipid Raman bands, being consistent with the known molecular composition of mitochondria. In addition, the second spectrum shows a strong band of ergosterol at 1602 cm^-1 , previously reported as "Raman spectroscopic signature of life of yeast."

Morphology-based classification of mycobacteria-infected macrophages with convolutional neural network: reveal EsxA-induced morphologic changes indistinguishable by naked eyes

EsxA is an essential virulence factor for Mycobacterium tuberculosis (Mtb) pathogenesis as well as an important biomarker for Mtb detection. In this study, we use light microscopy and deep learning-based image analysis to classify the morphologic changes of macrophages infected by Mycobacterium marinum (Mm), a surrogate model for Mtb. Macrophages were infected either with the mCherry-expressing Mm wild type strain (Mm(WT)), or a mutant strain with deletion of the esxA-esxB operon (Mm(ΔEsxA:B)). The mCherry serves as an infection marker to train the convolution neural network (CNN) and to validate the classification results. Data show that CNN can distinguish the Mm(WT)-infected cells from uninfected cells with an accuracy of 92.4% at 2 hours postinfection (hpi). However, the accuracy at 12 and 24 hpi is decreased to ∼75% and ∼83%, respectively, suggesting dynamic morphologic changes through different stages of infection. The accuracy of discriminating Mm(ΔEsxA:B)-infected cells from uninfected cells is lower than 80% at all time, which is consistent to attenuated virulence of Mm(ΔEsxA:B). Interestingly, CNN distinguishes Mm(WT)-infected cells from Mm(ΔEsxA:B)-infected cells with ∼90% accuracy, implicating EsxA induces unique morphologic changes in macrophages. Deconvolutional analysis successfully reconstructed the morphologic features used by CNN for classification, which are indistinguishable to naked eyes and distinct from intracellular mycobacteria. This study presents a deep learning-aided imaging analytical tool that can accurately detect virulent mycobacteria-infected macrophages by cellular morphologic changes. The observed morphologic changes induced by EsxA warrant further studies to fill the gap from molecular actions of bacterial virulence factors to cellular morphology.

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.

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.

Mitochondrial Imaging with Combined Fluorescence and Stimulated Raman Scattering Microscopy Using a Probe of the Aggregation-Induced Emission Characteristic

In vivo quantitative measurement of biodistribution plays a critical role in the drug/probe development and diagnosis/treatment process monitoring. In this work, we report a probe, named AIE-SRS-Mito, for imaging mitochondria in live cells via fluorescence (FL) and stimulated Raman scattering (SRS) imaging. The probe features an aggregation-induced emission (AIE) characteristic and possesses an enhanced alkyne Raman peak at 2223 cm^-1. The dual-mode imaging of AIE-SRS-Mito for selective mitochondrion-targeting was examined on a homemade FL-SRS microscope system. The detection limit of the probe in the SRS imaging was estimated to be 8.5 μM. Due to the linear concentration dependence of SRS and inertness of the alkyne Raman signal to environmental changes, the intracellular distribution of the probe was studied, showing a local concentration of >2.0 mM in the mitochondria matrix, which was >100-fold higher than the incubation concentration. To the best of our knowledge, this is the first time that the local concentration of AIE molecules inside cells has been measured noninvasively and directly. Also, the nonquenching effect of such AIE molecules in cell imaging has been verified by the positive correlation of FL and SRS signals. Our work will encourage the utilization of SRS microscopy for quantitative characterization of FL probes or other nonfluorescent compounds in living biological systems and the development of FL-SRS dual-mode probes for specific biotargets.

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.

Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue

Hyperspectral imaging (HSI) techniques are useful for obtaining very detailed structural and compositional information from biomedical, pharmaceutical, or clinical samples, among others. The informative value of these methods can be further increased through the application of different HSI techniques and joint analysis of the data. However, interpretation and understanding of multimodal HSI have been impeded by difficulties in registration of the different HSI data sets and by the lack of integrative analysis concepts. Here, we introduce two-dimensional correlation spectroscopy (2DCOS) as a novel technique for jointly analyzing HSI data which allows one to obtain deeper insights into the chemistry of complex samples by decrypting auto- and heterospectral correlations that may exist between features of the different HSI data. The general workflow of 2DCOS analysis is demonstrated by HSI examples acquired from cryo-sections of hamster brain tissue using Fourier-transform infrared (FT-IR) microspectroscopy, confocal Raman microspectroscopy (CRM), and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Multimodal hyperspectral image analysis by 2DCOS opens up new opportunities for spectral band assignments and thus the interpretation of structure-spectra and composition-spectra relationships. We foresee wide application potential for describing complex samples in various fields ranging from biomedicine to industrial applications.

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.

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.

Quantitative multi-image analysis for biomedical Raman spectroscopic imaging

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.

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.