Photodynamic therapy (PDT) is an established treatment which uses a photosensitiser drug and light source to destroy superficial lesions. This therapy is not applicable to deep-seated tumours due to limited light penetration. Recently, it has been found that replacing light in PDT with X-rays (thus named radiodynamic therapy (RDT)) can stimulate nano formulated photosensitiser drugs including Verteporfin and generate reactive oxygen species to kill the cancer cells. Herein, we investigated aspects of cellular metabolic processes after RDT in comparison with PDT and radiotherapy using label-free hyperspectral autofluorescence microscopy and image analysis. Biochemical signatures of metabolically relevant fluorophores (NAD(P)H, flavins, and optical-redox-ratio) were identified by developing a semi-unsupervised unmixing method combining supervised and unsupervised unmixing in a novel way.
Reactive oxygen species (ROS) play an essential role as cellular messengers, functioning as redox regulators under normal physiological conditions. However, the excessive production of ROS in cells and organs due to disorders such as diabetes mellitus, inflammation, cardiovascular, cancer, and neurodegenerative disease leads to oxidative stress, which may be an early indication of progressive pathology . Currently, ROS -specific indicators which requires labelling are used for ROS quantification. Therefore, a label-free imaging technique is desirable for assessing the level of ROS. In this work, we introduced a novel imaging method to quantitatively identify ROS in cells and tissues named autofluorescence multispectral imaging (AFMI). This technique involved a custom-built spectral imaging system with 18 spectral channels with distinctive excitation and emission wavelength spanning specific excitation (365 nm-495 nm) and emission (420 nm-700 nm) wavelength ranges. Such a system can extract rich spectral information related to the ROS present in cells and tissue. We correlated the spectral information obtained from AFMI to the level of ROS acquired from CellROX imaging, which served the reference ROS value in this study. Further, our analyses were repeated for UV sensitive applications where an excitation spectrum less than 400nm was avoided. Quantitative analysis of the spectral images showed a strong multispectral signature correlating the spectral variable with the ROS level in the cells and tissue. Our results showed that ROS levels can be determined non-invasively using AFMI, which potentially can be translated to future clinical applications where ROS are known to correlate with progressive disease.
Our approach to multispectral fluorescence microscopy can non-invasively identify the biomolecular composition of cells and capture complex biological heterogeneity which is fundamentally important for biological research and medical diagnostics. We have applied this technology to demonstrate the embryo quality for chromosomal abnormalities (containing euploid and aneuploidy cells) and understanding the biochemical signatures of polycystic ovarian syndrome (PCOS) oocytes. We then explored oocyte quality following treatment with the NAD+ precursor NMN. These findings demonstrate the utility of our approach to the multispectral assessment of autofluorescence for the non-destructive, label-free assessment of clinically relevant problems.
Pain is currently assessed using subjective measurements, often not aligning with clinical symptoms. Therefore, objective pain level assessments, using minimally-invasive and molecular methods, are needed to assess disease activity and response to treatment in osteoarthritis and rheumatoid arthritis. We report sophisticated quantitative biochemical “signatures” from the label-free hyperspectral imaging (HSI) of cartilage tissue for the characterization of molecular composition, structure and functional status. Further study on sinuvium tissue provides evidence that HSI could be used as a novel technique to delineate disease state. Additionally, HSI could be used to objectively separate individuals based on pain severity providing molecular correlates of pain.
Type 1 diabetes occurs when insulin secreting beta cells in pancreatic islets are destroyed leading to elevated glucose and ill health. Islet transplantation is an effective therapy, but islets are often damaged by the isolation process resulting in numerous, repeated transplants to achieve insulin independence. We have applied hyperspectral microscopy to damaged islets and have shown through the assessment of native cell autofluorescence we can detect heterogenous forms of damage (elevated ROS, inflammatory signalling and warm ischemia) in mouse islets. This approach has great potential to be translated clinically to minimise the burden of suboptimal islet transplantations.
Automated, unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biomedical research. Label-free imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors. Our multispectral fluorescence imaging technique allows precise quantification of native fluorophores in cells and tissues. This study uses label-free multispectral analysis to extract different fluorophores and redox ratio from single cells (oocytes, cultured cancer cells) as well as blastocyst embryos. Additionally, we characterise the molecular composition, structure and functional status of ex vivo healthy bovine and osteoarthritic human knee articular cartilage to assess 2 types of experimental treatments.
Multispectral assessment of cell autofluorescence gives a direct window into the molecular processes occurring within those cells. This can be used to non-invasively characterise and classify various cellular properties without requiring fixation, dyes or transformation. Human mesenchymal stem/ stromal cells (MSCs) have great potential to contribute to regenerative medicine, especially with regards to autologous transplantation. However, this capacity is often limited by inherent properties of cell lines, which prevent their being sufficiently expanded after derivation for effective clinical application. The investigation of these properties requires numerous, time and labour-intensive assays. In this study we have used correlative microscopy based on multispectral images of cell autofluorescence then correlated to functional assays in order to construct multispectral signatures of numerous inherent cell characteristics. These included cell cycle status (indicating the proportion of cells undergoing cell division at a given time), cell ‘age’ (number of passages undergone, indicating capacity for further expansion), and β- galactosidase (a marker of senescence, indicating cells which can no longer divide). This study has established a single protocol, in place of multi-functional assays, to characterize the growth and differentiation capacity of hMSC lines using a non-invasive approach.
Despite its wide-spread use, the success rate of assisted reproductive technologies including in vitro fertilization is less than 20%. Most human embryos are mosaic for chromosome abnormalities: containing cells that are euploid (normal) and aneuploid (incorrect number of chromosomes). Currently, a cell biopsy is used in IVF clinics to diagnose aneuploidy in the embryo but this does not provide a diagnosis of how many cells are aneuploid in the entire embryo. Hence, the development of a non-invasive tool to determine the proportion of aneuploid cells would likely improve IVF success. Aneuploidy in human embryos leads to altered metabolism. The co-factors utilized in cellular metabolism are autofluorescent and can be used to predict the metabolic state of cells. Here we used hyperspectral imaging to noninvasively assess intracellular fluorophores and thus metabolism. In this study, we utilized a powerful model of embryo aneuploidy where we generate mouse embryos with differing ratios of euploid:aneuploid cells. We also used primary human fibroblast cells with known aneuploidies to make comparison with euploid cells. Hyperspectral imaging of 1:3 chimeric embryos showed a distinct spectral profile compare to the control/normal embryos. The abundance of FAD in the inner cell mass (cells that form the fetus) of euploid and aneuploid blastocysts was strikingly different. For human cell lines, we were able to clearly distinguish between euploid and aneuploid with different karyotypes. These data show hyperspectral imaging is able to distinguish cells based on their ploidy status making it a promising tool in assessing embryo mosaicism.
Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biological research and medical diagnostics. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However, extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we developed a multispectral fluorescence imaging technique which allows precise quantification of the native fluorophores in cells and tissues. With that approach we are now able to non-invasively image the aspects of biomolecular composition of cells and tissues; where many of these fluorophores (NADH, flavins, cytochrome C) are relevant to metabolism. We will discuss label-free detection of reactive oxygen species (ROS) and the cell cycle. Cell cycle and metabolism have a tight, bidirectional relationship, with the ability of the cell to commit to growth depending on the availability of metabolites, and the molecular mechanisms of the cell-cycle being linked to the regulation of metabolic networks. Cells entering the cell cycle increase glycolysis as they go from G1-phase into S-phase, this results in accumulation of the NADH relative to FAD which is also fluorescent.
Moreover, metabolic dysregulation is common across the spectrum of diseases, this next-generation methodology is able to detect major health conditions including neurodegeneration and cancer. This work also reports on approaches for early diagnosis of motor neurone disease (MND) and localisation of cancer margins for ocular surface squamous neoplasia. Our optimal discrimination approach (extracted features for treatment monitoring in MND and melanoma) enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent.
Despite its wide-spread use, the success rate of assisted reproductive technologies including IVF is less than 20% in Australia/New Zealand. Most early human embryos are mosaic for chromosome abnormalities, containing a proportion of normal and abnormal cells. The most common chromosomal abnormality is aneuploidy: incorrect number of chromosomes. This form of mosaicism is thought account for early pregnancy loss in IVF. Current single cell biopsies of embryos are not diagnostic for the proportion of cells that are aneuploid (degree of heterogeneity/mosaicism). Thus, development of a non-invasive tool to determine the proportion of aneuploid cells facilitating segregation of embryos with a low percentage of aneuploid cells would likely improve IVF success rates. In other cells, including cancer cells, aneuploidy results in altered cellular metabolism. In this study we utilised hyperspectral imaging as a means of non-invasively measuring cellular metabolism in the early embryo. We utilised a mouse model where we manipulated the ratio of aneuploid:normal cells. Aneuploid embryos were generated by treatment during division from 4 to 8 cells using a reversible spindle assembly check point inhibitor, reversine. Eight-cell aneuploid embryos were dissociated and joined with control/normal cells to generate 1:1 aneuploid:normal chimeras. Hyperspectral imaging of 1:1 chimeric embryos had a distinct spectral profile that varied dramatically from the control/normal embryos. Interestingly, entirely aneuploid embryos showed a spectral profile dissimilar from both normal and chimeric embryos. These data show hyperspectral imaging is capable of distinguishing between embryos with varying degrees of aneuploidy making it a promising tool in assessing embryo health.
Degradation of cartilage, occurring in osteoarthritis and other conditions leads to pain and reduced mobility. Current treatments beyond anti-inflammatories include intra-articular injections of hyaluronan or preparations based on adult mesenchymal stem cells (MSC), the latter shown to aid cartilage regeneration which requires the assessment of the cartilage, best on a molecular level and in a minimally invasive way. However, the conventional methods are invasive, destroying and can only provide a snapshot of a tissue structure and functional state on a sample-by-sample basis, while the continuous monitoring and high-throughput assays require low-invasive biopsy-free approach. As a first step to address this problem, in current work, we explored the potential of label-free multispectral imaging of endogenous tissue fluorescence to characterise the molecular composition, structure and functional status of ex vivo healthy bovine and osteoarthritic (OA) human knee articular cartilage followed by monitoring the effects of experimental treatment of OA cartilage performed ex vivo.
However, strong autofluorescence of collagens (especially from collagen type II, which is the structural backbone of collagen fibrils) from various cartilage layers presents a challenge, because this signal tends to overpower the fluorescence from chondrocytes. We have managed to use Robust Dependent Component Analysis (RoDECA) to observe the detailed metabolic information with a proper account of intrinsic cellular heterogeneity, which signifies the sophisticated quantitative biochemical analysis. This work reports on the “signatures” of the healthy articular cartilage for superficial and transitional layer, define the “healthy range” of each fluorophore’s abundance and localization of chondrocytes non-invasively as well as identify the changes of the signatures in OA cartilage of real patients and observe the reaction of the OA cartilage on 2 types of experimental treatments.
Adipose-derived stem cells (ADSC) based therapies have the potential to treat cartilage disorders such as osteoarthritis in both animals and humans. The current opinion points to secretions from stem cells in the proximity of the diseased joints as key agents in these new regenerative treatments. In this work, biological insights are derived from native fluorescence by using hyperspectral imaging technique. This approach allows to noninvasively monitor the relative levels of individual biochemicals in the tissue, at a cellular level. In this work, we used hyperspectral imaging and unsupervised unmixing to characterize the effects of specific cytokine treatments on different cartilage layers. These layers are composed of various types of collagen, elastin as well as chondrocytes. Strong autofluorescence of the cartilage chip especially from collagen type II presents a significant challenge for the hyperspectral unmixing analysis because the background signal from the collagen tends to dominate the autofluorescence from the chondrocytes. The latter is used for therapy monitoring, and it allows to compare the merits of specific regenerative treatments of cartilage.
The unsupervised hyperspectral unmixing approach developed in this work, Robust Dependent Component Analysis (RoDECA) provides a robust and detailed biochemical information from the examined cells and tissues, with a proper account of intrinsic cellular heterogeneity. With appropriate hardware adjustment and software modification in this cartilage chip model study, it was possible to analyze the biochemical effects of the stem cell based treatments. Most importantly, collagen I and collagen II have been distinguished for the first time in a label- free manner, which gives a new way of observing the regenerative treatments of the articular cartilage. This method can be extended in the future to the analysis of optically thick tissue.
Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous fluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from imaging of native fluorescence has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Multispectral intrinsic fluorescence imaging was applied to patient olfactory neurosphere-derived cells, cell model of a human metabolic disease MELAS (mitochondrial myopathy, encephalomyopathy, lactic acidosis, stroke-like syndrome). By using an endogenous source of contrast, subtle metabolic variations have been detected between living cells in their full morphological context which made it possible to distinguish healthy from diseased cells before and after therapy. Cellular maps of native fluorophores, flavins, bound and free NADH and retinoids unveiled subtle metabolic signatures and helped uncover significant cell subpopulations, in particular a subpopulation with compromised mitochondrial function. The versatility of our method is further illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes, and assessing the condition of preimplantation embryos. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent.
Extracting biochemical information from tissue autofluorescence is a promising approach to non-invasively monitor disease treatments at a cellular level, without using any external biomarkers. Our recently developed unsupervised hyperspectral unmixing by Dependent Component Analysis (DECA) provides robust and detailed metabolic information with proper account of intrinsic cellular heterogeneity. Moreover this method is compatible with established methods of fluorescent biomarker labelling.
Recently adipose-derived stem cell (ADSC) – based therapies have been introduced for treating different diseases in animals and humans. ADSC have been shown promise in regenerative treatments for osteoarthritis and other bone and joint disorders. One of the mechanism of their action is their anti-inflammatory effects within osteoarthritic joints which aid the regeneration of cartilage. These therapeutic effects are known to be driven by secretions of different cytokines from the ADSCs. We have been using the hyperspectral unmixing techniques to study in-vitro the effects of ADSC-derived cytokine-rich secretions with the cartilage chip in both human and bovine samples. The study of metabolic effects of different cytokine treatment on different cartilage layers makes it possible to compare the merits of those treatments for repairing cartilage.
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