Towards Raman-Based Screening of Acute Lymphoblastic Leukemia-Type B (B-ALL) Subtypes

Evaluating the accuracy of Raman spectroscopy in differentiating leukemia patients from healthy individuals: A systematic review and meta-analysis

PURPOSE

To assess the accuracy of Raman spectroscopy in distinguishing between patients with leukemia and healthy individuals.

METHOD

PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases were searched for relevant articles published from inception of the respective database to November 1, 2023. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), were calculated along with their corresponding 95 % confidence intervals (CI). A summary comprehensive receiver operating characteristic curve (SROC) was constructed and the area under the curve (AUC) was calculated. The degree of heterogeneity was tested and analyzed.

RESULTS

Fifteen groups of original studies from 13 articles were included. The pooled SEN and SPE were 0.93 (95 % CI, [0.92 -0.93]) and 0.91(95 % CI, [0.90-0.92]), respectively. The DOR was 613.01 (95 %CI, [270.79-1387.75]), and the AUC was 0.99. The Deeks' funnel plot asymmetry test indicated no significant publication bias among the included studies (bias coefficient, 40.80; P = 0.13 < 0.10). The meta-regression analysis findings indicated that the observed heterogeneity could be attributed to variations in sample categories and Raman spectroscopy techniques.

CONCLUSION

We confirmed that Raman spectroscopy has good accuracy in differentiating patients with leukemia from healthy individuals, and may become a means of leukemia screening in clinical practice. In the case of analysis based on live cells using surface-enhanced Raman spectroscopy (SERS) improved diagnostic efficacy was observed.

High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.

Raman spectroscopy can recognize the KMT2A rearrangement as a distinct subtype of leukemia

Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are the two most common hematologic malignancies, challenging to treat and associated with high recurrence and mortality rates. This work aims to identify specific Raman biomarkers of ALL cells with the KMT2A gene rearrangement (KMT2A-r), representing a highly aggressive subtype of childhood leukemia with a poor prognosis. The proposed approach combines the sensitivity and specificity of Raman spectroscopy with machine learning and allows us to distinguish not only myelo- and lymphoblasts but also discriminate B-cell precursor (BCP) ALL with KMT2A-r from other blasts of BCP-ALL. We have found that KMT2A-r ALL cells fixed with 0.5% glutaraldehyde exhibit a unique spectroscopic profile that enables us to identify this subtype from other leukemias and normal cells. Therefore, a rapid and label-free method was developed to identify ALL blasts with KMT2A-r based on the ratio of the two Raman bands assigned to phenylalanine - 1040 and 1008 cm-1. This is the first time that a particular group of leukemic cells has been identified in a label-free way. The identified biomarker can be used as a screening method in diagnostic laboratories or non-reference medical centers.

Alterations in lipid metabolism accompanied by changes in protein and carotenoid content as spectroscopic markers of human T cell activation

This work aims to understand better the mechanism of cellular processes accompanying the activation of human T cells and to develop a novel, fast, label-free approach to identify molecular biomarkers for this process. The standard methodology for confirming the activation state of T cells is based on flow cytometry and using antibodies recognizing activation markers. The method provide high specificity detection but may be susceptible to background staining or non-specific secondary antibody reactions. Here, we evaluated the potential of Raman-based molecular imaging in distinguishing non-activated and activated human T cells. Confocal Raman microscopy was performed on T cells followed by chemometrics to obtain comprehensive molecular information, while Stimulated Raman Scattering imaging was used to quickly provide high-resolution images of selected cellular components of activated and non-activated cells. For the first time, carotenoids, lipids, and proteins were shown to be important biomarkers of T-cell activation. We found that T-cell activation was accompanied by lipid accumulation and loss of carotenoid content. Our findings on the biochemical, morphological, and structural changes associated with activated mature T cells provide insights into the molecular changes that occur during therapeutic manipulation of the immune response. The methodology for identifying activated T cells is based on a novel imaging method and supervised and unsupervised chemometrics. It unambiguously identifies specific and unique molecular changes without the need for staining, fixation, or any other sample preparation.

Automatic subtyping of Diffuse Large B-cell Lymphomas (DLBCL): Raman-based genetic and metabolic classification

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma in adults, is a genetically and metabolically heterogeneous group of aggressive malignancies. The complexity of their molecular composition and the variability in clinical presentation make clinical diagnosis and treatment selection a serious challenge. The challenge is therefore to quickly and correctly classify DLBCL cells. In this work, we show that Raman imaging is a tool with high diagnostic potential, providing unique information about the biochemical components of tumor cells and their metabolism. We present models of classification of lymphoma cells based on their Raman spectra. The models automatically and efficiently identify DLBCL cells and assign them to a given cell-of-origin (COO) subtype (activated B cell-like (ABC) or germinal center B cell-like (GCB)) or, respectively, to a comprehensive cluster classification (CCC) subtype (OxPhos/non-OxPhos). In addition, we describe each lymphoma subtype by its unique spectral profile, linking it to biochemical, genetic, or metabolic features.

Raman classification of selected subtypes of acute lymphoblastic leukemia (ALL)

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) with chromosome translocations like KMT2A gene rearrangement (KMT2A-r) and BCR-ABL1 fusion gene have been recognized as crucial drivers in both BCP-ALL leukemogenesis and treatment management. Standard diagnostic protocols for proliferative diseases of the hematopoietic system, like KMT2A-r-ALL, are genetically based and strongly molecularly oriented. Therefore, an efficient diagnostic procedure requires not only experienced and multidisciplinary laboratory staff but also considerable instrumentation and material costs. In recent years, a Raman spectroscopy method has been increasingly used to detect subtle chemical changes in individual cells resulting from stress or disease. Therefore, the objective of this study was to identify Raman signatures for the molecular subtypes and to develop a classification method based on the unique spectroscopic profile of in vitro models that represent specific aberrations aimed at KMT2A-r (RS4;11, and SEM) and the BCR-ABL1 fusion gene (SUP-B15, BV-173, and SD-1). Data analysis was based on chemometric methods, i.e. principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM). The PCA-based multivariate model was used for pattern recognition of each investigated group of cells while PLS-DA and SVM were used to build models for the discrimination of spectra from the studied BCP-ALL molecular subtypes. The results showed that the studied molecular subtypes of ALL have characteristic spectroscopic profiles reflecting their peculiar biochemical state. The content of lipids (1600 cm-1), nucleic acids (789 cm-1), and haemoproteins (754, 1130, and 1315 cm-1), which are crucial in cell metabolism, was indicated as the main source of differentiation between subtypes. Identification of spectroscopic markers of cells with BCR-ABL1 or KMT2A-r may be useful in pharmacological studies to monitor the effectiveness of chemotherapy and further to understand differences in molecular responses between leukemia primary cells and cell lines.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Reliable cell preparation protocol for Raman imaging to effectively differentiate normal leukocytes and leukemic blasts

Leukemias are a remarkably diverse group of malignancies originating from abnormal progenitor cells in the bone marrow. Leukemia subtypes are classified according to the cell type that has undergone neoplastic transformation using demanding and time-consuming methods. Alternative is Raman imaging that can be used both for living and fixed cells. However, considering the diversity of leukemic cell types and normal leukocytes, and the availability of different sample preparation protocols, the main objective of this work was to verify them for leukemia and normal blood cell samples for Raman imaging. The effect of glutaraldehyde (GA) fixation in a concentration gradient (0.1 %, 0.5 %, and 2.5 % GA) on the molecular structure of T-cell acute lymphoblastic leukemia (T-ALL) and peripheral blood mononuclear cells (PBMCs) was verified. Changes in the secondary structure of proteins within cells were indicated as the main effect of fixation, as shown by an increase in band intensity at 1041 cm^-1, characteristic for in-plane δ(CH) deformation in phenylalanine (Phe). Different sensitivity of mononuclear and leukemic cells to fixation was observed. While the 0.1 % concentration of GA was too low to preserve the cell structure for an extended period of time, a GA concentration of 0.5 % seemed optimal for both normal and malignant cells. Chemical changes in PBMCs samples stored for 11 days were also investigated, which manifested in numerous modifications in the secondary structure of proteins and the content of nucleic acids. The impact of cell preculturing for 72 h after unbanking was verified, and there was no significant effect on the molecular structure of cells fixed with 0.5 % GA. In summary, the developed protocol for the preparation of samples for Raman imaging allows for the effective differentiation of fixed normal leukocytes from malignant T lymphoblasts.

Advances in the application of Raman spectroscopy in haematological tumours

Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.