Integration of diffraction phase microscopy and Raman imaging for label-free morpho-molecular assessment of live cells

Quantitative phase microscopies: accuracy comparison

Quantitative phase microscopies (QPMs) play a pivotal role in bio-imaging, offering unique insights that complement fluorescence imaging. They provide essential data on mass distribution and transport, inaccessible to fluorescence techniques. Additionally, QPMs are label-free, eliminating concerns of photobleaching and phototoxicity. However, navigating through the array of available QPM techniques can be complex, making it challenging to select the most suitable one for a particular application. This tutorial review presents a thorough comparison of the main QPM techniques, focusing on their accuracy in terms of measurement precision and trueness. We focus on 8 techniques, namely digital holographic microscopy (DHM), cross-grating wavefront microscopy (CGM), which is based on QLSI (quadriwave lateral shearing interferometry), diffraction phase microscopy (DPM), differential phase-contrast (DPC) microscopy, phase-shifting interferometry (PSI) imaging, Fourier phase microscopy (FPM), spatial light interference microscopy (SLIM), and transport-of-intensity equation (TIE) imaging. For this purpose, we used a home-made numerical toolbox based on discrete dipole approximation (IF-DDA). This toolbox is designed to compute the electromagnetic field at the sample plane of a microscope, irrespective of the object's complexity or the illumination conditions. We upgraded this toolbox to enable it to model any type of QPM, and to take into account shot noise. In a nutshell, the results show that DHM and PSI are inherently free from artefacts and rather suffer from coherent noise; In CGM, DPC, DPM and TIE, there is a trade-off between precision and trueness, which can be balanced by varying one experimental parameter; FPM and SLIM suffer from inherent artefacts that cannot be discarded experimentally in most cases, making the techniques not quantitative especially for large objects covering a large part of the field of view, such as eukaryotic cells.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Cell-TIMP: Cellular Trajectory Inference based on Morphological Parameter

Cellular morphology, shaped by various genetic and environmental influences, is pivotal to studying experimental cell biology, necessitating precise measurement and analysis techniques. Traditional approaches, which rely on geometric metrics derived from stained images, encounter obstacles stemming from both the imaging and analytical domains. Staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Our approach revealed a clear pattern of morphological evolution tied to the diseases' advancement, highlighting the efficacy of our method in identifying functionally significant shape changes where conventional techniques falter. This advancement offers a fresh perspective on analyzing cellular morphology and holds significant potential for the broader research community, enabling a deeper understanding of complex biological dynamics.

Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

Injectable amnion hydrogel-mediated delivery of adipose-derived stem cells for osteoarthritis treatment

Current treatment strategies for osteoarthritis (OA) predominantly address symptoms with limited disease-modifying potential. There is a growing interest in the use of adipose-derived stem cells (ADSCs) for OA treatment and developing biomimetic injectable hydrogels as cell delivery systems. Biomimetic injectable hydrogels can simulate the native tissue microenvironment by providing appropriate biological and chemical cues for tissue regeneration. A biomimetic injectable hydrogel using amnion membrane (AM) was developed which can self-assemble in situ and retain the stem cells at the target site. In the present study, we evaluated the efficacy of intraarticular injections of AM hydrogels with and without ADSCs in reducing inflammation and cartilage degeneration in a collagenase-induced OA rat model. A week after the induction of OA, rats were treated with control (phosphate-buffered saline), ADSCs, AM gel, and AM-ADSCs. Inflammation and cartilage regeneration was evaluated by joint swelling, analysis of serum by cytokine profiling and Raman spectroscopy, gross appearance, and histology. Both AM and ADSC possess antiinflammatory and chondroprotective properties to target the sites of inflammation in an osteoarthritic joint, thereby reducing the inflammation-mediated damage to the articular cartilage. The present study demonstrated the potential of AM hydrogel to foster cartilage tissue regeneration, a comparable regenerative effect of AM hydrogel and ADSCs, and the synergistic antiinflammatory and chondroprotective effects of AM and ADSC to regenerate cartilage tissue in a rat OA model.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

Preparation and characterization of amnion hydrogel and its synergistic effect with adipose derived stem cells towards IL1β activated chondrocytes

Inflammation leads to chondrocyte senescence and cartilage degeneration, resulting in osteoarthritis (OA). Adipose‐derived stem cells (ADSCs) exert paracrine effects protecting chondrocytes from degenerative changes. However, the lack of optimum delivery systems for ADSCs limits its use in the clinic. The use of extracellular matrix based injectable hydrogels has gained increased attention due to their unique properties. In the present study, we developed hydrogels from amnion tissue as a delivery system for ADSCs. We investigated the potential of amnion hydrogel to maintain ADSC functions, the synergistic effect of AM with ADSC in preventing the catabolic responses of inflammation in stimulated chondrocytes. We also investigated the role of Wnt/β-catenin signaling pathway in IL-1β induced inflammation in chondrocytes and the ability of AM-ADSC to inhibit Wnt/β-catenin signaling. Our results showed that AM hydrogels supported cell viability, proliferation, and stemness. ADSCs, AM hydrogels and AM-ADSCs inhibited the catabolic responses of IL-1β and inhibited the Wnt/β-catenin signaling pathway, indicating possible involvement of Wnt/β-catenin signaling pathways in IL-1β induced inflammation. The results also showed that the synergistic effect of AM-ADSCs was more pronounced in preventing catabolic responses in activated chondrocytes. In conclusion, we showed that AM hydrogels can be used as a potential carrier for ADSCs, and can be developed as a potential therapeutic agent for treating OA.

Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning

Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays introduced by the specimen, has been demonstrated to discern cellular phenotypes based on differential morphological attributes. Rapid acquisition capability and the availability of label-free images with high information content have enabled researchers to use machine learning (ML) to reveal latent features. We developed a set of ML classifiers, including convolutional neural networks, to discern healthy B cells from lymphoblasts and classify stages of B cell acute lymphoblastic leukemia. Here, we show that the average dry mass and volume of normal B cells are lower than those of cancerous cells and that these morphologic parameters increase further alongside disease progression. We find that the relaxed training requirements of a ML approach are conducive to the classification of cell type, with minimal space, training time, and memory requirements. Our findings pave the way for a larger study on clinical samples of acute lymphoblastic leukemia, with the overarching goal of its broader use in hematopathology, where the prospect of objective diagnoses with minimal sample preparation remains highly desirable.

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning

CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.