Biology subjects

Habibalahi, A.

Publications and source records attributed to Habibalahi, A..

8 recordsLinked to original sources

bioRxiv2024

Integration of hyperspectral imaging and transcriptomics from individual cells with HyperSeq

Microscopy and omics are complementary approaches to probe the molecular state of cells in health and disease, combining granularity with scalability. While important advances have been achieved over the last decade in each area, integrating both imaging- and sequencing-based assays on the same cell has proven challenging. In this study, a new approach called HyperSeq that combines hyperspectral autofluorescence imaging with transcriptomics on the same cell is demonstrated. HyperSeq was applied to Michigan Cancer Foundation 7 (MCF-7) breast cancer cells and identified a subpopulation of cells exhibiting bright autofluorescence rings at the plasma membrane in optical channel 13 ({lambda}ex = 431 nm,{lambda} em = 594 nm). Correlating the presence of a ring with the gene expression in the same cell indicated that ringed cells are more likely to express hallmark genes of apoptosis and less likely to express genes associated with ATP production. Further, correlation of cell morphology with gene expression suggested that multiple members of the spliceosome were downregulated in larger MCF-7 cells. Multiple genes were evenly expressed across cell sizes but also exhibited higher usage of specific exons in larger or smaller cells. Finally, correlation between gene expression and fluorescence within the spectral range of Nicotinamide adenine dinucleotide hydrogen (NADH) provided insight into the metabolic states of MCF-7 cells. These observations provided a link between the cells optical spectrum and its internal molecular state, demonstrating the utility of HyperSeq to study cell biology at single cell resolution by integrating spectral, morphological and transcriptomic analyses into a single, streamlined workflow.

bioinformatics↗

bioRxiv2023

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using patient cells with the same evaluation technique. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

biophysics↗

bioRxiv2022

Automated identification of aneuploid cells within the inner cell mass of an embryo using a numerical extraction of morphological signatures

STUDY QUESTIONCan artificial intelligence distinguish between euploid and aneuploid cells within the inner cell mass of mouse embryos using brightfield images? SUMMARY ANSWERA deep morphological signature (DMS) generated by deep learning followed by swarm intelligence and discriminative analysis can identify the ploidy state of inner cell mass (ICM) in the mouse blastocyst-stage embryo. WHAT IS KNOWN ALREADYThe presence of aneuploidy - a deviation from the expected number of chromosomes - is predicted to cause early pregnancy loss or congenital disorders. To date, available techniques to detect embryo aneuploidy in IVF clinics involve an invasive biopsy of trophectoderm cells or a non-invasive analysis of cell-free DNA from spent media. These approaches, however, are not specific to the ICM and will consequently not always give an accurate indication of the presence of aneuploid cells with known ploidy therein. STUDY DESIGN, SIZE, DURATIONThe effect of aneuploidy on the morphology of ICMs from mouse embryos was studied using images taken using a standard brightfield microscope. Aneuploidy was induced using the spindle assembly checkpoint inhibitor, reversine (n = 13 euploid and n = 9 aneuploid). The morphology of primary human fibroblast cells with known ploidy was also assessed. PARTICIPANTS/MATERIALS, SETTING, METHODSTwo models were applied to investigate whether the morphological details captured by brightfield microscopy could be used to identify aneuploidy. First, primary human fibroblasts with known karyotypes (two euploid and trisomy: 21, 18, 13, 15, 22, XXX and XXY) were imaged. An advanced methodology of deep learning followed by swarm intelligence and discriminative analysis was used to train a deep morphological signature (DMS). Testing of the DMS demonstrated that there are common cellular features across different forms of aneuploidy detectable by this approach. Second, the same approach was applied to ICM images from control and reversine treated embryos. Karyotype of ICMs was confirmed by mechanical dissection and whole genome sequencing. MAIN RESULTS AND THE ROLE OF CHANCEThe DMS for discriminating euploid and aneuploid fibroblasts had an area under the receiver operator characteristic curve (AUC-ROC) of 0.89. The presence of aneuploidy also had a strong impact on ICM morphology (AUC-ROC = 0.98). Aneuploid fibroblasts treated with reversine and projected onto the DMS space mapped with untreated aneuploid fibroblasts, supported that the DMS is sensitive to aneuploidy in the ICMs, and not a non-specific effect of the reversine treatment. Consistent findings in different contexts suggests that the role of chance low. LARGE SCALE DATAN/A LIMITATIONS, REASON FOR CAUTIONConfirmation of this approach in humans is necessary for translation. WIDER IMPLICATIONS OF THE FINDINGSThe application of deep learning followed by swarm intelligence and discriminative analysis for the development of a DMS to detect euploidy and aneuploidy in the ICM has high potential for clinical implementation as the only equipment it requires is a brightfield microscope, which are already present in any embryology laboratory. This makes it a low cost, a non-invasive approach compared to other types of pre-implantation genetic testing for aneuploidy. This study gives proof of concept for a novel strategy with the potential to enhance the treatment efficacy and prognosis capability for infertility patients. STUDY FUNDING/COMPETING INTEREST(S)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), the National Health and Medical Research Council (APP2003786) and an ARC Discovery Project (DP210102960). The authors declare that there is no conflict of interest.

bioinformatics↗

bioRxiv2022

Oocyte and cumulus cell cooperativity and metabolic plasticity under the direction of oocyte paracrine factors

Mammalian oocytes develop and mature in a mutually dependent relationship with surrounding cumulus cells. The oocyte actively regulates cumulus cell differentiation and function by secreting soluble paracrine oocyte-secreted factors (OSFs). We characterized the molecular mechanisms by which two model OSFs, cumulin and BMP15, regulate oocyte maturation and cumulus-oocyte cooperativity. Exposure to these OSFs during maturation altered the proteomic and multispectral autofluorescence profiles of both the oocyte and cumulus cells. In oocytes, cumulin significantly upregulated proteins involved in nuclear function. In cumulus cells, both OSFs elicited marked upregulation of a variety of metabolic processes (mostly anabolic), including lipid, nucleotide, and carbohydrate metabolism, while mitochondrial metabolic processes were downregulated. The mitochondrial changes were validated by functional assays confirming altered mitochondrial morphology, respiration, and content, whilst maintaining ATP homeostasis. Collectively, these data demonstrate that OSFs remodel cumulus cell metabolism during oocyte maturation in preparation for ensuing fertilization and embryonic development. HIGHLIGHTSO_LIDuring oocyte maturation, oocyte-secreted factors promote cell cooperativity between the oocyte and cumulus cells by altering the molecular composition of both cell types. C_LIO_LIOocyte-secreted factors downregulate protein catabolic processes, and upregulate DNA binding, translation, and ribosome assembly in oocytes. C_LIO_LIOocyte-secreted factors alter mitochondrial number, morphology, and function in cumulus cells. C_LIO_LIOocyte-secreted factors further enhance metabolic plasticity in cumulus cells by upregulating anabolic pathways for macromolecules and small molecule organics. C_LIO_LIThe oocyte, via oocyte-secreted factors, instructs cumulus cells to increase metabolic workload on its behalf, thereby subduing oocyte metabolism. C_LI

developmental biology↗

bioRxiv2022

moRphology - dEep Learning Imaging Cells (RELIC) - to Differentiate Between Normal and Pathological Kidney Exfoliated Cells

Chronic kidney disease (CKD) is characterised by progressive loss of kidney function leading to kidney failure. Significant kidney damage can occur before symptoms are detected. Currently, kidney tissue biopsy is the gold standard for evaluation of renal damage and CKD severity. This study explores how to precisely quantify morphology characteristics of kidney cells exfoliated into urine, with a view to establish a future urine-based non-invasive diagnostic for CKD. We report the development of a novel deep learning method, which was able to discover a RELIC (moRphology dEep Learning Imaging Cells) signature that can differentiate between kidney cells exfoliated in human urine of and CKD patients with varying degree of kidney damage and non-CKD controls. Exfoliated proximal tubule cells (PTCs) originating from kidneys were isolated from the urine of patients with different levels of kidney damage using previously published methods. An advanced combination of artificial intelligence techniques, deep learning, swarm intelligence, and discriminative analysis was used to discover a RELIC signature in brightfield microscopy images of exfoliated PTCs. Kidney damage in the study subjects was characterised by assessing kidney tissues obtained through a nephrectomy or kidney biopsy. A special deep learning algorithm was developed and trained to create a predictive tool. The algorithm was then used to analyse data from patients with normal and fibrotic kidneys. Data were then classified according to different groups (healthy or fibrosis) and clustering of the training and validation cells was determined for model validation. We developed a novel deep learning method, to obtain RELIC signatures and identify specific deep morphological features which can be used to differentiate urinary PTC cells shed by people with CKD (confirmed by tissue histology obtained from an invasive kidney biopsy) from those without CKD, with a discriminatory accuracy of 82%. We identified a RELIC signature which can be used on a collection of bright field images of exfoliated urinary PTCs to create a predictive tool and differentiate between normal and pathological kidney cells. This study, for the first time, provides a proof of concept that urinary exfoliated tubule cells in patients with kidney fibrosis and healthy controls differ in appearance (morphology) as observed under a basic brightfield microscope. The results suggest that morphological signatures of exfoliated PTCs have the potential to serve as a non-invasive marker of kidney fibrosis.

bioinformatics↗

bioRxiv2020

Non-invasive, label-free optical analysis to detect aneuploidy within the inner cell mass of the preimplantation embryo

Study questionCan label-free, non-invasive optical imaging by hyperspectral microscopy discern between euploid and aneuploid cells within the inner cell mass of the mouse preimplantation embryo? Summary answerHyperspectral microscopy shows a variance in metabolic activity which enables discrimination between euploid and aneuploid cells. What is known alreadyEuploid/aneuploid mosaicism affects up to 17.3% of human blastocyst embryos with trophectoderm biopsy or spent media currently utilised to diagnose aneuploidy and mosaicism in clinical in vitro fertilisation. Based on their design, these approaches will fail to diagnose the presence or proportion of aneuploid cells within the fetal lineage (inner cell mass (ICM)) of some blastocyst embryos. Study design, size, durationThe impact of aneuploidy on cellular metabolism of primary human fibroblast cells and mouse embryos was assessed by 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 (euploid n= 467; aneuploid n= 969). For mouse embryos, 50-70 individual euploid and aneuploid blastomeres (8-cell stage embryo) and chimeric blastocysts (40-50 per group: euploid; aneuploid; or 1:1 and 1:3 ratio of euploid:aneuploid) were utilised for hyperspectral imaging. Participants/materials, setting, methodsTwo 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 4-to 8-cell division. Individual blastomeres were dissociated from reversine treated (aneuploid) and control (euploid) 8-cell embryos and either imaged directly or used to generate chimeric blastocysts with differing ratios of euploid:aneuploid cells. Individual blastomeres and embryos were subjected to hyperspectral imaging. Changes in cellular metabolism were determined by quantification of metabolic cofactors (inferred from their autofluorescence signature): reduced nicotinamide adenine dinucleotide (NAD(P)H), flavins with the subsequent calculation of the optical redox ratio (ORR: Flavins/[NAD(P)H + Flavins]). Mathematical algorithms were applied to extract features from the autofluorescence signals of each cell/blastomere/inner cell mass to discriminate between euploid and aneuploid. Main results and the role of chanceAn increase in the relative abundance of NAD(P)H with a decrease in flavins led to a significant reduction in the ORR for aneuploid cells in both primary human fibroblasts and individual mouse blastomeres (P < 0.05). Mathematical algorithms were able to achieve good separation between (i) euploid and aneuploid primary human fibroblast cells, (ii) euploid and aneuploid mouse blastomeres cells and (iii) euploid and aneuploid chimeric blastocysts and (iv) 1:1 and 1:3 chimeric blastocysts. The accuracy of these separations was supported by receiver operating characteristic curves with areas under the curve of 0.85, 0.99, 0.87 and 0.88, respectively. We believe that the role of chance is low as multiple cellular models (human somatic cells and mouse embryos) demonstrated a consistent shift in cellular metabolism in response to aneuploidy as well as the robust capacity of mathematical features to separate euploid and aneuploid cells in a statistically significant manner. Limitations, reasons for cautionThere would be added value in determining the degree of embryo mosaicism by sequencing the inner cell mass (ICM) of individual blastocysts to correlate with metabolic profile and level of discrimination achieved using the mathematical features approach. Wider implications of the findingsHyperspectral imaging was able to discriminate between euploid and aneuploid human fibroblasts and mouse embryos. This may lead to the development of an accurate and non-invasive optical approach to assess mosaicism within the ICM of human embryos in the absence of fluorescent tags. Study funding/competing interest(s)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 (CEI40100003). The authors declare that there is no conflict of interest.

cell biology↗

bioRxiv2020

Non-invasive real-time imaging of reactive oxygen species (ROS) using multispectral auto-fluorescence imaging technique: a novel tool for redox biology

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.

cell biology↗

bioRxiv2019

NAD+ repletion rescues female fertility during reproductive ageing

Female infertility is a common and devastating condition with life-long health, emotional and social consequences. There is currently no pharmacological therapy for preserving oocyte quality during aging, which is the strongest risk factor for infertility. This leads to an age dependent decline in natural conception and IVF success rates (1). Here, we show that this is due in part to declining levels of the metabolic cofactor nicotinamide adenine dinucleotide (NAD+), and that restoring NAD+ levels with its metabolic precursor nicotinamide mononucleotide (NMN) rejuvenates oocyte quality and quantity in aged animals, leading to improved fertility. These benefits extend to the developing embryo, where NMN supplementation in embryo culture media following IVF enhances blastocyst formation in older mice. The NAD+ dependent deacylase SIRT2 is sufficient, but not essential, to recapitulate the benefits of in vivo NMN treatment, and transgenic overexpression of SIRT2 maintains oocyte spindle assembly, accurate chromosome segregation, decreased oxidative stress and overall fertility with ageing. Pharmacological elevation of NAD+ may be an effective, non-invasive strategy for restoring and maintaining female fertility during ageing, and for improving the success of IVF.

developmental biology↗