Predicting Metastasis from Cues of Metastatic Cancer Stem-like Cells-3D-Ultrasensitive Metasensor at a Single-Cell Level

Abstract

Metastasis is the primary reason for treatment failure and cancer-related deaths. Hence forecasting the disease in its primary state can advance the prognosis. However, existing techniques fail to reveal the tumor heterogeneity or its evolutionary cascades; hence they are not feasible to predict the onset of metastatic cancer. The key to metastasis originates from the primary tumor cells, evolving by inheriting multistep sequential cue signals. We have identified this specific population, termed metastatic cancer stem-like cells (MCSCs), to foresee cancer’s ability to metastasize. An invasive property renders MCSCs nonadherent, summoning a powerful technique to forecast metastasis. Thus, we have generated an ultrasensitive 3D-metasensor to efficiently capture and investigate MCSCs and magnify the vital premetastatic signals from a single cell. We developed 3D-metasensor by an ultrafast laser ionization technique, consisting of self-assembled three-dimensionally organized nanoprobes incorporated with dopant functionalities. This distinct methodology establishes attachment with nonadherent MCSCs, elevates Raman activity, and enables probing of consequent signals (metabolic, proliferation, and metastatic) specifically altered in MCSCs. Extensive analysis using prediction toolsthe area under the curve (AUC) and principal component analysis (PCA)revealed high sensitivity (100%) and specificity (80%) to differentiate the MCSCs from other populations. Further, investigation reveals that the cue signal level from MCSCs of primary cancer is analogous to MCSCs from higher-level tumors, disclosing the relative dependence to estimate the primary tumor’s capacity to metastasize. Multiple spectrum evaluation using the metasensor pinpoint the dynamic cues in MCSCs predict the onset of metastasis; thus, exploring these metastasis hallmarks can enhance prognosis and revolutionize therapy strategies.

Dual Functional Full-Color Carbon Dot-Based Organelle Biosensor Array for Visualization of Lipid Droplet Subgroups with Varying Lipid Composition in Living Cells

“…To observe the diversity of lipid composition in LDs, we counted the three-channel fluorescence intensity of 221 LDs in four cell lines (Figure S17) and performed the cluster visualization analysis on the data set . The principal component analysis (PCA) method was used for the overall intercellular relationship study . As shown in Figure C, the LDs from HepG2 and SKOV3 cells are divided into two distinct clusters, while those from HeLa and LoVo cells overlap with the two clusters to varying degrees.…”

Human metabolite detection by surface-enhanced Raman spectroscopy

“…(D) Dopant-functionalized 3D-metasensors and PCA from complete SERS spectra of different cell populations (Reproduced with permission from Ref. [ 96 ]. Copyright 2021, American Chemical Society.).…”

“…SERS metabolite detection could also be applied in the prediction of cancer metastasis. Tumor metastasis generally originates from primary cancer cells, and in this evolution process, a specific population called metastatic cancer stem cell-like cells (MCSC) was considered to be related to cancer metastasis [ 96 ]. Dharmalingam et al.…”

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

“…6B ). 108 By focusing on specific Raman regions corresponding to metabolite (800–1450 cm −1 ) and proliferative-metastatic (1000–1650 cm −1 ) signals, PCA reveals distinct clustering of metastatic cells from their progeny and non-stem types. This validates the presence of multiplex hallmark signals, which is attributed to concentration variations of metabolites such as glucose, pyruvate and phenylalanine, as inherent cues to predict the metastatic ability of a tumor.…”