Detection of the T cell activation state using nonlinear optical microscopy

Harnessing Raman spectroscopy for cell therapy bioprocessing

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Lipid content evaluation of Drosophila tumour associated haemocytes through Third Harmonic Generation measurements

Non-linear microscopy is a powerful imaging tool to examine structural properties and subcellular processes of various biological samples. The competence of Third Harmonic Generation (THG) includes the label free imaging with diffraction-limited resolution and three-dimensional visualization with negligible phototoxicity effects. In this study, THG records and quantifies the lipid content of Drosophila haemocytes, upon encountering normal or tumorigenic neural cells, in correlation with their shape or their state. We show that the lipid accumulations of adult haemocytes are similar before and after encountering normal cells. In contrast, adult haemocytes prior to their interaction with cancer cells have a low lipid index, which increases while they are actively engaged in phagocytosis only to decrease again when haemocytes become exhausted. This dynamic change in the lipid accrual of haemocytes upon encountering tumour cells could potentially be a useful tool to assess the phagocytic capacity or activation state of tumour-associated haemocytes.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Raman spectroscopy provides valuable process insights for cell-derived and cellular products

Two of the big challenges in modern bioprocesses are process economics and in-depth process understanding. Getting access to online process data helps to understand process dynamics and monitor critical process parameters (CPPs). This is an important part of the quality-by- design concept that was introduced to the pharmaceutical industry in the last decade. Raman spectroscopy has proven to be a versatile tool to allow noninvasive measurements and access to a broad spectrum of analytes. This information can then be used for enhanced process control strategies. This review article will focus on the latest applications of Raman spectroscopy in established protein production bioprocesses as well as show its potential for virus, cell therapy, and mRNA processes.

Touch-free optical technologies to streamline the production of T cell therapies

Currently approved adoptive T cell therapy relies on autologous (obtained from the same patient) T cells, which often suffer from poor quality that diminishes treatment efficacy. Due to the heterogeneous nature of T cell quality between and within patients, significant efforts are aimed at optimizing cell manipulation and growth conditions for potent T cell products. We believe that touch-free imaging and sensing technologies are critical to monitor single-cell features during T cell manufacturing to ensure consistent and optimally timed methods for cell manipulation and growth. Here, we discuss emerging label-free optical imaging and sensing methods, along with machine learning techniques that could enable in-line feedback to optimize T cell quality at multiple stages during manufacturing. These methods have the potential to streamline current workflow, accelerate the manufacture of safe high-quality T cell therapies, and improve our understanding of the dynamic, heterogeneous processes of T cell manufacturing.

Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells

Particles in biopharmaceutical formulations remain a hot topic in drug product development. With new product classes emerging it is crucial to discriminate particulate active pharmaceutical ingredients from particulate impurities. Technical improvements, new analytical developments and emerging tools (e.g., machine learning tools) increase the amount of information generated for particles. For a proper interpretation and judgment of the generated data a thorough understanding of the measurement principle, suitable application fields and potential limitations and pitfalls is required. Our review provides a comprehensive overview of novel particle analysis techniques emerging in the last decade for particulate impurities in therapeutic protein formulations (protein-related, excipient-related and primary packaging material-related), as well as particulate biopharmaceutical formulations (virus particles, virus-like particles, lipid nanoparticles and cell-based medicinal products). In addition, we review the literature on applications, describe specific analytical approaches and illustrate advantages and drawbacks of currently available techniques for particulate biopharmaceutical formulations.

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Application of a deep-learning technique to non-linear images from human tissue biopsies for shedding new light on breast cancer diagnosis

The development of label-free non-invasive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II and III. By the implementation of a convolutional neural network (CNN) model, the differentiation of malignant from benign breast tissue samples and the discrimination of the different grades of cancer in a fast and accurate way were achieved. The obtained results provide a step ahead towards the use of optical diagnostic tools in conjunction with the CNN image classifier for the reliable and rapid malignancy diagnosis in clinic.

Polarization-dependent second-harmonic generation for collagen-based differentiation of breast cancer samples

Nonlinear optical imaging techniques have been widely used to reveal biological structures for accurate diagnosis at the cellular as well as the tissue level. In the present study, polarization-dependent second-harmonic generation (PSHG) was used to determine collagen orientation in breast cancer biopsy tissues (grades 0, I, II and III). The obtained data were processed using fast Fourier transform (FFT) analysis, while second-harmonic generation (SHG) anisotropy and the "ratio parameter" values were also calculated. Such measurements were shown to be able to distinguish collagen structure modifications in different cancer grades tested. The analysis presented herein suggests that PSHG imaging could provide a quantitative evaluation of the tumor state and the distinction of malignant from benign breast tissues. The obtained results also allowed the development of a biophysical model, which can explain the aforementioned differentiations and is in agreement with the simulations relating the SHG anisotropy values with the mechanical tension applied to the collagen during cancer progression. The current approach could be a step forward for the development of new, nondestructive, label free optical diagnostic tools for cancer reducing the need of recalls and unnecessary biopsies, while potentially improving cancer detection rates.

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

SIGNIFICANCE

Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.

AIM

We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.

APPROACH

The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.

RESULTS

In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.

CONCLUSIONS

Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.

Classification of T-cell activation via autofluorescence lifetime imaging

The function of a T cell depends on its subtype and activation state. Here, we show that the imaging of autofluorescence-lifetime signals from quiescent and activated T cells can be used to classify the cells. T cells isolated from human peripheral blood and activated in culture via a tetrameric antibody against the surface ligands CD2, CD3 and CD28 showed specific activation-state-dependent patterns of autofluorescence lifetime. Logistic-regression models and random-forest models classified T cells according to activation state with 97–99% accuracy, and according to activation state (quiescent or activated) and subtype (CD3⁺ CD8⁺ or CD3⁺ CD4⁺) with 97% accuracy. Autofluorescence-lifetime imaging could be used to non-destructively determine T-cell function.

Third Harmonic Generation microscopy distinguishes malignant cell grade in human breast tissue biopsies

The ability to distinguish and grade malignant cells during surgical procedures in a fast, non-invasive and staining-free manner is of high importance in tumor management. To this extend, Third Harmonic Generation (THG), Second Harmonic Generation (SHG) and Fourier-Transform Infrared (FTIR) spectroscopy were applied to discriminate malignant from healthy cells in human breast tissue biopsies. Indeed, integration of non-linear processes into a single, unified microscopy platform offered complementary structural information within individual cells at the submicron level. Using a single laser beam, label-free THG imaging techniques provided important morphological information as to the mean nuclear and cytoplasmic area, cell volume and tissue intensity, which upon quantification could not only distinguish cancerous from benign breast tissues but also define disease severity. Simultaneously, collagen fibers that could be detected by SHG imaging showed a well structured continuity in benign tumor tissues, which were gradually disoriented along with disease severity. Combination of THG imaging with FTIR spectroscopy could provide a clearer distinction among the different grades of breast cancer, since FTIR analysis showed increased lipid concentrations in malignant tissues. Thus, the use of non-linear optical microscopy can be considered as powerful and harmless tool for tumor cell diagnostics even during real time surgery procedures.

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.