FTIR Spectroscopic Imaging Supports Urine Cytology for Classification of Low- and High-Grade Bladder Carcinoma

Quantification and profiling of urine cells by integrated cytocentrifugation and infrared spectroscopy

The presence of cells in urine and in particular White Blood Cells (WBCs) is often associated with Urinary Tract Infections (UTIs) and other diseases. Non-invasive screening of WBCs requires the development of cost-effective point of care diagnostic tools. Infrared (IR) spectroscopy has the potential to identify and quantify cells in urine. However, the quantification of cells by compact IR spectrophotometers can be hindered by the presence of highly concentrated interfering biomolecules. The use of separation procedures can assist in identifying and quantifying cells but reduces the point of care capabilities of the technology. In this study, we propose coupling cytocentrifugation with transflection IR spectroscopy for the isolation and quantification of cells in urine. Urine samples were spiked with monocytes and T-lymphocytes, cyto-centrifuged onto low-e slides and measured in transflection mode. An optional cell clean-up step, either performed before (by resuspending in PBS) or after the cytocentrifugation (by soaking the slide in water), was evaluated. In a first experiment using monocytes, IR band areas were linear (R2 = 0.98) in the 8 × 103-2 × 105 cells mL-1 range, thus demonstrating the detection of cells at pathological numbers (pyuria, i.e., >104 WBCs mL-1). Secondly, to mimic real samples with varying cell types, urine samples containing both monocytes and T-lymphocytes were analysed to determine their concentration simultaneously. Partial Least Squares (PLS) regression enabled the simultaneous quantification of two types of different cells, yielding prediction errors of 2 × 104 cells mL-1 for monocytes and 4 × 104 cells mL-1 for T-lymphocytes. The results suggest that the technique has the potential to be implemented as a fast, simple, versatile, and cost-effective method for quantifying and profiling cells in urine.

Adipose-derived mesenchymal stem cells' adipogenesis chemistry analyzed by FTIR and Raman metrics

The full understanding of molecular mechanisms of cell differentiation requires a holistic view. Here we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell differentiation and identifying biochemical heterogeneity. Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergoing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofluorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to identify spectral predictors of adipogenesis and test machine-learning (ML) methods (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs differentiation degree. IRRSmetrics4stem provided insights into the chemism of adipogenesis. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% predictive efficiency of the selected ML methods proved the high sensitivity of the IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation. This study introduced a new bioassay identifying molecular markers indicating molecular transformations and delivering rapid and machine learning-based monitoring of adipogenesis that can be relevant to other differentiation processes. Thus, we introduce a novel, rapid, machine learning-based bioassay to identify molecular markers of adipogenesis. It can be relevant to identification of differentiation-related molecular processes in other cell types, and beyond the cell differentiation including progression of different cellular pathophysiologies reconstituted in vitro.

Detection of Sialic Acid to Differentiate Cervical Cancer Cell Lines Using a Sambucus nigra Lectin Biosensor

Pap smear screening is a widespread technique used to detect premalignant lesions of cervical cancer (CC); however, it lacks sensitivity, leading to identifying biomarkers that improve early diagnosis sensitivity. A characteristic of cancer is the aberrant sialylation that involves the abnormal expression of α2,6 sialic acid, a specific carbohydrate linked to glycoproteins and glycolipids on the cell surface, which has been reported in premalignant CC lesions. This work aimed to develop a method to differentiate CC cell lines and primary fibroblasts using a novel lectin-based biosensor to detect α2,6 sialic acid based on attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and chemometric. The biosensor was developed by conjugating gold nanoparticles (AuNPs) with 5 µg of Sambucus nigra (SNA) lectin as the biorecognition element. Sialic acid detection was associated with the signal amplification in the 1500-1350 cm-1 region observed by the surface-enhanced infrared absorption spectroscopy (SEIRA) effect from ATR-FTIR results. This region was further analyzed for the clustering of samples by applying principal component analysis (PCA) and confidence ellipses at a 95% interval. This work demonstrates the feasibility of employing SNA biosensors to discriminate between tumoral and non-tumoral cells, that have the potential for the early detection of premalignant lesions of CC.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

BACKGROUND

Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing.

RESULT

Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate，which indicates that it is an extremely promising auxiliary technology for cancer diagnosis.

SIGNIFICANCE

The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].

Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review

The incidence of thyroid nodules (TNs) is estimated at 36.5% and 23% in females and males, respectively. A single thyroid nodule is usually detected during ultrasound assessment in patients with symptoms of thyroid dysfunction or neck mass. TNs are classified as benign tumours (non-malignant hyperplasia), benign neoplasms (e.g., adenoma, a non-invasive follicular tumour with papillary nuclear features) or malignant carcinomas (follicular cell-derived or C-cell derived). The differential diagnosis is based on fine-needle aspiration biopsies and cytological assessment (which is burdened with the bias of subjectivity). Raman spectroscopy (RS) is a laser-based, semiquantitative technique which shows for oscillations of many chemical groups in one label-free measurement. RS, through the assessment of chemical content, gives insight into tissue state which, in turn, allows for the differentiation of disease on the basis of spectral characteristics. The purpose of this study was to report if RS could be useful in the differential diagnosis of TN. The Web of Science, PubMed, and Scopus were searched from the beginning of the databases up to the end of June 2023. Two investigators independently screened key data using the terms "Raman spectroscopy" and "thyroid". From the 4046 records found initially, we identified 19 studies addressing the differential diagnosis of TNs applying the RS technique. The lasers used included 532, 633, 785, 830, and 1064 nm lines. The thyroid RS investigations were performed at the cellular and/or tissue level, as well as in serum samples. The accuracy of papillary thyroid carcinoma detection is approx. 90%. Furthermore, medullary, and follicular thyroid carcinoma can be detected with up to 100% accuracy. These results might be biased with low numbers of cases in some research and overfitting of models as well as the reference method. The main biochemical changes one can observe in malignancies are as follows: increase of protein, amino acids (like phenylalanine, tyrosine, and tryptophan), and nucleic acid content in comparison with non-malignant TNs. Herein, we present a review of the literature on the application of RS in the differential diagnosis of TNs. This technique seems to have powerful application potential in thyroid tumour diagnosis.