Detecting reactive oxygen species (ROS) that play a critical role as redox modulators and signalling molecules in biological systems currently requires invasive methods such as ROS -specific indicators for imaging and quantification. We developed a non-invasive, real-time, label-free imaging technique for assessing the level of ROS in live cells and thawed cryopreserved tissues that is compatible with in-vivo imaging. The technique is based on autofluorescence multispectral imaging (AFMI) carried out in an adapted fluorescence microscope with an expanded number of spectral channels spanning specific excitation (365 nm-495 nm) and emission (420 nm-700 nm) wavelength ranges. We established a strong quantitative correlation between the spectral information obtained from AFMI and the level of ROS obtained from CellROX staining. The results were obtained in several cell types (HeLa, PANC1 and mesenchymal stem cells) and in live kidney tissue. Additioanly,two spectral regimes were considered: with and without UV excitation (wavelengths > 400 nm); the latter being suitable for UV-sensitive systems such as the eye. Data were analyzed by linear regression combined with an optimization method of swarm intelligence. This allowed the calibration of AFMI signals to the level of ROS with excellent correlation (R = 0.84, p = 0.00) in the entire spectral range and very good correlation (R = 0.78, p = 0.00) in the limited, UV-free spectral range. We also developed a strong classifier which allowed us to distinguish moderate and high levels of ROS in these two regimes (AUC = 0.91 in the entire spectral range and AUC = 0.78 for UV-free imaging). These results indicate that ROS in cells and tissues can be imaged non-invasively, which opens the way to future clinical applications in conditions where reactive oxygen species are known to contribute to progressive disease such as in ophthalmology, diabetes, kidney disease, cancer and neurodegenerative diseases.

Can label-free, non-invasive optical imaging by hyperspectral autofluorescence microscopy discern between euploid and aneuploid cells within the inner cell mass (ICM) of the mouse preimplantation embryo? Hyperspectral autofluorescence microscopy enables discrimination between euploid and aneuploid ICM in mouse embryos. Euploid/aneuploid mosaicism affects up to 17.3% of human blastocyst embryos with trophectoderm biopsy or spent media currently utilized to diagnose aneuploidy and mosaicism in clinical in vitro fertilization. Based on their design, these approaches will fail to diagnose the presence or proportion of aneuploid cells within the foetal lineage ICM of some blastocyst embryos. The impact of aneuploidy on cellular autofluorescence and metabolism of primary human fibroblast cells and mouse embryos was assessed using a fluorescence microscope adapted for imaging with multiple spectral channels (hyperspectral imaging). Primary human fibroblast cells with known ploidy were subjected to hyperspectral imaging to record native cell fluorescence (4–6 independent replicates, euploid n = 467; aneuploid n = 969). For mouse embryos, blastomeres from the eight-cell stage (five independent replicates: control n = 39; reversine n = 44) and chimeric blastocysts (eight independent replicates: control n = 34; reversine n = 34; 1:1 (control:reversine) n = 30 and 1:3 (control:reversine) n = 37) were utilized for hyperspectral imaging. The ICM from control and reversine-treated embryos were mechanically dissected and their karyotype confirmed by whole genome sequencing (n = 13 euploid and n = 9 aneuploid). Two models were employed: (i) primary human fibroblasts with known karyotype and (ii) a mouse model of embryo aneuploidy where mouse embryos were treated with reversine, a reversible spindle assembly checkpoint inhibitor, during the four- to eight-cell division. Individual blastomeres were dissociated from control and reversine-treated eight-cell embryos and either imaged directly or used to generate chimeric blastocysts with differing ratios of control:reversine-treated cells. Individual blastomeres and embryos were interrogated by hyperspectral imaging. Changes in cellular metabolism were determined by quantification of metabolic co-factors (inferred from their autofluorescence signature): NAD(P)H and flavins with the subsequent calculation of the optical redox ratio (ORR: flavins/[NAD(P)H + flavins]). Autofluorescence signals obtained from hyperspectral imaging were examined mathematically to extract features from each cell/blastomere/ICM. This was used to discriminate between different cell populations. An increase in the relative abundance of NAD(P)H and decrease in flavins led to a significant reduction in the ORR for aneuploid cells in primary human fibroblasts and reversine-treated mouse blastomeres (P < 0.05). Mathematical analysis of endogenous cell autofluorescence achieved separation between (i) euploid and aneuploid primary human fibroblast cells, (ii) control and reversine-treated mouse blastomeres cells, (iii) control and reversine-treated chimeric blastocysts, (iv) 1:1 and 1:3 chimeric blastocysts and (v) confirmed euploid and aneuploid ICM from mouse blastocysts. The accuracy of these separations was supported by receiver operating characteristic curves with areas under the curve of 0.97, 0.99, 0.87, 0.88 and 0.93, respectively. We believe that the role of chance is low as mathematical features separated euploid from aneuploid in both human fibroblasts and ICM of mouse blastocysts. N/A. Although we were able to discriminate between euploid and aneuploid ICM in mouse blastocysts, confirmation of this approach in human embryos is required. While we show this approach is safe in mouse, further validation is required in large animal species prior to implementation in a clinical setting. We have developed an original, accurate and non-invasive optical approach to assess aneuploidy within the ICM of mouse embryos in the absence of fluorescent tags. Hyperspectral autofluorescence imaging was able to discriminate between euploid and aneuploid human fibroblast and mouse blastocysts (ICM). This approach may potentially lead to a new diagnostic for embryo analysis. K.R.D. is supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). This study was funded by the Australian Research Council Centre of Excellence for Nanoscale Biophotonics (CE140100003) and the National Health and Medical Research Council (APP2003786). The authors declare that there is no conflict of interest.

Clinical OSSN diagnostics by non-invasive spectral imaging of eye autofluorescence must be rapid enough to be comfortable for patients - without compromising accuracy. This requires identifying optimized spectral signatures of OSSN based on a minimal number of spectrally defined images. Here, we identified such signatures using a data-driven methodology of swarm intelligence. Ten patients with histopathological diagnosis of ocular surface squamous neoplasia (OSSN) were recruited. Their unstained biopsy OSSN specimens were investigated using a custom-built autofluorescence multispectral microscopy imaging system. The images were taken in 38 spectral channels spanning specific excitation (340 nm-510 nm) and emission (420 nm-650 nm) wavelength ranges. To identify optimized spectral signatures of OSSN from a small number of channels, swarm intelligence was combined with discriminative cluster analysis. This study established an optimized spectral signature of OSSN derived from multispectral data taken in 38 channels. Depending on the critical nature of the application and the consequences of misclassification error, two optimized spectral signatures with 5 and 10 channels were obtained which reduced the imaging time to 20 and 40 seconds, a reduction by 75% and 80 %, respectively. The K-nearest neighbor classifier was then built using OSSN spectral signatures and optimized to successfully detect OSSN with ~1% and ~14% misclassification error using 10 and 5 channels, respectively. Our study found an optimized spectral signature of OSSN allowing rapid diagnostic imaging in clinical settings and demonstrates the feasibility of using optimized multispectral autofluorescence spectral signatures to detect and determine boundaries of OSSN.

In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were imaged by multispectral microscopy. Tissue autofluorescence images were obtained with a custom-built fluorescent microscope with 59 spectral channels, each with specific excitation and emission wavelength ranges, suitable for the most abundant tissue fluorophores such as elastin, flavins, porphyrin, and lipofuscin. Images were analyzed using a new classification framework called fused-classification, designed to minimize interpatient variability, as an established support vector machine learning method. Normal, PTG, and OSSN regions were automatically detected and delineated, with accuracy evaluated against expert assessment by a specialist in OSSN pathology. Signals from spectral channels yielding signals from elastin, flavins, porphyrin, and lipofuscin were significantly different between regions classified as normal, PTG, and OSSN (p < 0.01). Differential diagnosis of PTG/OSSN and normal tissue had accuracy, sensitivity, and specificity of 88 ± 6%, 84 ± 10% and 91 ± 6%, respectively. Our automated diagnostic method generated maps of the reasonably well circumscribed normal/PTG and OSSN interface. PTG and OSSN margins identified by our automated analysis were in close agreement with the margins found in the H&E sections. Such a map can be rapidly generated on a real time basis and potentially used for intraoperative assessment.

The purpose of this study is to develop a deep radiomic signature based on an artificial intelligence (AI) model. This radiomic signature identifies oocyte morphological changes corresponding to reproductive aging in bright field images captured by optical light microscopy. Oocytes were collected from three mice groups: young (4- to 5-week-old) C57BL/6J female mice, aged (12-month-old) mice, and aged mice treated with the NAD+ precursor nicotinamide mononucleotide (NMN), a treatment recently shown to rejuvenate aspects of fertility in aged mice. We applied deep learning, swarm intelligence, and discriminative analysis to images of mouse oocytes taken by bright field microscopy to identify a highly informative deep radiomic signature (DRS) of oocyte morphology. Predictive DRS accuracy was determined by evaluating sensitivity, specificity, and cross-validation, and was visualized using scatter plots of the data associated with three groups: Young, old and Old + NMN. DRS could successfully distinguish morphological changes in oocytes associated with maternal age with 92% accuracy (AUC~1), reflecting this decline in oocyte quality. We then employed the DRS to evaluate the impact of the treatment of reproductively aged mice with NMN. The DRS signature classified 60% of oocytes from NMN-treated aged mice as having a ‘young’ morphology. In conclusion, the DRS signature developed in this study was successfully able to detect aging-related oocyte morphological changes. The significance of our approach is that DRS applied to bright field oocyte images will allow us to distinguish and select oocytes originally affected by reproductive aging and whose quality has been successfully restored by the NMN therapy.