Raman and autofluorescence spectrum dynamics along the HRG-induced differentiation pathway of MCF-7 cells

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Raman spectroscopy and machine learning for the classification of breast cancers

Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.

Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications

Raman spectroscopy has shown very promising results in medical diagnostics by providing label-free and highly specific molecular information of pathological tissue ex vivo and in vivo. Nevertheless, the high specificity of Raman spectroscopy comes at a price, i.e., low acquisition rate, no direct access to depth information, and limited sampling areas. However, a similar case regarding advantages and disadvantages can also be made for other highly regarded optical modalities, such as optical coherence tomography, autofluorescence imaging and fluorescence spectroscopy, fluorescence lifetime microscopy, second-harmonic generation, and others. While in these modalities the acquisition speed is significantly higher, they have no or only limited molecular specificity and are only sensitive to a small group of molecules. It can be safely stated that a single modality provides only a limited view on a specific aspect of a biological specimen and cannot assess the entire complexity of a sample. To solve this issue, multimodal optical systems, which combine different optical modalities tailored to a particular need, become more and more common in translational research and will be indispensable diagnostic tools in clinical pathology in the near future. These systems can assess different and partially complementary aspects of a sample and provide a distinct set of independent biomarkers. Here, we want to give an overview on the development of multimodal systems that use RS in combination with other optical modalities to improve the diagnostic performance.

Non-invasive cell classification using the Paint Raman Express Spectroscopy System (PRESS)

Raman scattering represents the distribution and abundance of intracellular molecules, including proteins and lipids, facilitating distinction between cellular states non-invasively and without staining. However, the scattered light obtained from cells is faint and cells have complex structures, making it difficult to obtain a Raman spectrum covering the entire cell in a short time using conventional methods. This also prevents efficient label-free cell classification. In the present study, we developed the Paint Raman Express Spectroscopy System, which uses two fast-rotating galvano mirrors to obtain spectra from a wide area of a cell. By using this system and applying machine learning, we were able to acquire broad spectra of a variety of human and mouse cell types, including pluripotent stem cells and confirmed that each cell type can be classified with high accuracy. Moreover, we classified different activation states of human T cells, despite their similar morphology. This system could be used for rapid and low-cost drug evaluation and quality management for drug screening in cell-based assays.

Automatic Determination of the Sequential Order of Dynamic Data and Its Application to Vibrational Spectroscopy

For over the past 30 years, generalized two-dimensional correlation spectroscopy has formed an active and widespread research area. One of the most attractive properties of this method is that one can determine the sequential order of signal changes. But the determination of the sequential order has only been done manually for several arbitrarily chosen bands. In this paper, we develop a method to automatically determine the sequential order of all of the band intensity changes, and we applied this method to band changes of vibration spectra. This method will open a door to analyze more complicated signals often seen in life phenomena.

Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis

Spectral pretreatments, such as background removal from Raman big data, are crucial to have a smooth link to advanced spectral analysis. Recently, we developed an automated background removal method, where we considered the shortest length of a spectrum by changing the scaling factor of the background spectrum. Here, we propose a practical way to correct the systematic error caused by noise from measurements. This correction has been realized to be more effective and accurate for automatic background removal.

Raman and infrared spectroscopy reveal that proliferating and quiescent human fibroblast cells age by biochemically similar but not identical processes

Dermal fibroblast cells can adopt different cell states such as proliferation, quiescence, apoptosis or senescence, in order to ensure tissue homeostasis. Proliferating (dividing) cells pass through the phases of the cell cycle, while quiescent and senescent cells exist in a non-proliferating cell cycle-arrested state. However, the reversible quiescence state is in contrast to the irreversible senescence state. Long-term quiescent cells transit into senescence indicating that cells age also when not passing through the cell cycle. Here, by label-free in vitro vibrational spectroscopy, we studied the biomolecular composition of quiescent dermal fibroblast cells and compared them with those of proliferating and senescent cells. Spectra were examined by multivariate statistical analysis using a PLS-LDA classification model, revealing differences in the biomolecular composition between the cell states mainly associated with protein alterations (variations in the side chain residues of amino acids and protein secondary structure), but also within nucleic acids and lipids. We observed spectral changes in quiescent compared to proliferating cells, which increased with quiescence cultivation time. Raman and infrared spectroscopy, which yield complementary biochemical information, clearly distinguished contact-inhibited from serum-starved quiescent cells. Furthermore, the spectra displayed spectral differences between quiescent cells and proliferating cells, which had recovered from quiescence. This became more distinct with increasing quiescence cultivation time. When comparing proliferating, (in particular long-term) quiescent and senescent cells, we found that Raman as well as infrared spectroscopy can separate these three cellular states from each other due to differences in their biomolecular composition. Our spectroscopic analysis shows that proliferating and quiescent fibroblast cells age by similar but biochemically not identical processes. Despite their aging induced changes, over long time periods quiescent cells can return into the cell cycle. Finally however, the cell cycle arrest becomes irreversible indicating senescence.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages

PURPOSE

To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules.

METHODS

The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples.

RESULTS

Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning.

CONCLUSIONS

This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

Discrimination analysis of excitatory and inhibitory neurons using Raman spectroscopy

We have succeeded in discriminating between intact excitatory and inhibitory neuronal cells with Raman analysis.

A Proposal for Automated Background Removal of Bio-Raman Data

The subtraction of background components from observed spectra is essential in performing multivariable analysis, frequently applied to Raman spectra of cells. The subtraction procedure, however, is manual and time consuming, especially for big data. Here, we propose an automated method for removing background information from measured spectra of cells, exhibiting the theoretical framework and an experimental application.

Raman spectral dynamics of single cells in the early stages of growth factor stimulation

Cell fates change dynamically in response to various extracellular signals, including growth factors that stimulate differentiation and proliferation. The processes underlying cell-fate decisions are complex and often include large cell-to-cell variations, even within a clonal population in the same environment. To understand the origins of these cell-to-cell variations, we must detect the internal dynamics of single cells that reflect their changing chemical milieu. In this study, we used the Raman spectra of single cells to trace their internal dynamics during the early stages of growth factor stimulation. This method allows nondestructive and inclusive time-series analyses of chemical compositions of the same single cells. Applying a Gaussian mixture model to the major principal components of the single-cell Raman spectra, we detected the dynamics of the chemical states in MCF-7 cancer-derived cells in the absence and presence of differentiation and proliferation factors. The dynamics displayed characteristic variations according to the functions of the growth factors. In the differentiation pathway, the chemical composition changed directionally between multiple states, including both reversible and irreversible state transitions. In contrast, in the proliferation pathway, the chemical composition was homogenized into a single state. The differentiation factor also stimulated fluctuations in the chemical composition, whereas the proliferation factor did not.