Analyzing normal proliferating, hypoxic and necrotic regions of T-47D human breast cancer spheroids using Raman spectroscopy

Talari, Abdullah Chandra Sekhar; Raza, Ahtasham; Rehman, Shazza; Rehman, Ihtesham Ur

doi:10.1080/05704928.2017.1363053

Cited by 16 publications

(15 citation statements)

References 46 publications

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“…These results suggest that Raman micro-spectroscopy may be suitable for identifying different primary HBEC cell cultures, which in future could be applied to identify different lung cell types within co-cultures and to study the process of early carcinogenesis in lung cell culture, of particular interest in the field of cancer research. Further refinements may also enable application in three dimensional lung cultures, which are increasingly being used in basic cell biology studies as they have been recognized to better recapitulate the physiological conditions and environments a cell experiences in vivo 36 , 37 .…”

Section: Discussionmentioning

confidence: 99%

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Surmacki

Woodhams

Haslehurst

et al. 2018

Sci Rep

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Live cell Raman micro-spectroscopy is emerging as a promising bioanalytical technique for label-free discrimination of a range of different cell types (e.g. cancer cells and fibroblasts) and behaviors (e.g. apoptosis). The aim of this study was to determine whether confocal Raman micro-spectroscopy shows sufficient sensitivity and specificity for identification of primary human bronchial epithelial cells (HBECs) to be used for live cell biological studies in vitro. We first compared cell preparation substrates and media, considering their influence on lung cell proliferation and Raman spectra, as well as methods for data acquisition, using different wavelengths (488 nm, 785 nm) and scan protocols (line, area). Evaluating these parameters using human lung cancer (A549) and fibroblast (MRC5) cell lines confirmed that line-scan data acquisition at 785 nm using complete cell media on a quartz substrate gave optimal performance. We then applied our protocol to acquisition of data from primary human bronchial epithelial cells (HBEC) derived from three independent sources, revealing an average sensitivity for different cell types of 96.3% and specificity of 95.2%. These results suggest that Raman micro-spectroscopy is suitable for delineating primary HBEC cell cultures, which in future could be used for identifying different lung cell types within co-cultures and studying the process of early carcinogenesis in lung cell culture.

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Section: Discussionmentioning

confidence: 99%

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Surmacki

Woodhams

Haslehurst

et al. 2018

Sci Rep

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“…Our previous studies have used AI-and ML-based systematic intelligent control-based approaches for identification of normal cell lines from breast cancer lines, hypoxic region from proliferation region, and even identification of single cell in 3D scaffold. These approaches have helped in visualization of multiple components of single cell and tissue biopsies by means of fuzzy control, genetic algorithm, PCA, LDA, and cluster analysis [30][31][32]. In our previous studies, RS and mathematical modeling approaches were employed to characterize and differentiate two breast cancer and one normal breast cell lines (MDA-MB-436, MCF-7, and MCF-10A).…”

Section: Introductionmentioning

confidence: 99%

“…A combination of Raman and AI and ML approaches were used to identify chemical changes associated with normal proliferating, hypoxic, and necrotic regions of T-47D human breast cancer spheroid model. Raman markers such as low amide I and high tryptophan content were differentiating three regions of spheroids [31]. RS was also employed to identify chemical changes in ductal carcinoma in situ and invasive carcinoma using breast tissue biopsies.…”

Section: Introductionmentioning

confidence: 99%

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Talari

Rehman

2019

Expert Review of Molecular Diagnostics

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Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring. ARTICLE HISTORY

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“…In these experiments, ~10‐mW power was used, and it has not caused any damage to cells, and it has demonstrated in previous communications. Visible inspections and real‐time measurements in spectral changes like shifting to lower cm −1 , peak width increasing, and background appearance were used to measure sample sensitivity to laser power …”

Section: Methodsmentioning

confidence: 99%

Single‐cell Raman microscopy of microengineered cell scaffolds

Baldock

Talari

Smith

et al. 2018

J Raman Spectroscopy

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Studying cells in a three-dimensional (3D) environment has great potential in understanding cell behaviours such as morphology, proliferation, differentiation, and migration. Microengineered 3D cell scaffolds with precise defined geometries have offered a new approach to study cell behaviour and its interactions with scaffolds. The use of Raman spectroscopy to characterise biomolecules is a rapidly expanding area and has been implemented in numerous fields including pharmacology, microbiology, toxicology, and single-cell studies.However, one area where it remains unexploited despite the vast potential of the technique is in the investigation of 3D cell scaffolds. A combination of Raman microscopy and chemometric approaches have employed to investigate the structure and biochemistry of nanofabricated scaffolds and a cell-scaffold complex. The 3D Raman mapping combined with the use of nanofabricated 3D scaffolds offers a unique opportunity to assess the influence of scaffold architecture on cell body and cell nuclei morphology and biochemistry. For the first time, we have cultured a human epithelial colorectal adenocarcinoma cell line on OrmoComp scaffolds and determined the structure and biochemistry of nanofabricated scaffolds and a cell-scaffold complex with the use of Raman microscopy combined with appropriate data analysis protocols. The results demonstrate the potential of 3D Raman mapping for identifying biochemical and physical variation within single cells as they grow and adhere to 3D scaffolds.

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Analyzing normal proliferating, hypoxic and necrotic regions of T-47D human breast cancer spheroids using Raman spectroscopy

Cited by 16 publications

References 46 publications

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Single‐cell Raman microscopy of microengineered cell scaffolds

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