Heterogeneous Biological Samples Research Articles

Challenges and advances for huntingtin detection in cerebrospinal fluid: in support of relative quantification.

Huntington disease (HD) is a progressive and devastating neurodegenerative disease caused by expansion of a glutamine-coding CAG tract in the huntingtin (HTT) gene above a critical threshold of ~35 repeats resulting in expression of mutant HTT (mHTT). A promising treatment approach being tested in clinical trials is HTT lowering, which aims to reduce levels of the mHTT protein. Target engagement of these therapies in the brain are inferred using antibody-based assays to measure mHTT levels in the cerebrospinal fluid (CSF), which is frequently reported as absolute mHTT concentration based on a monomeric protein standard used to generate a standard curve. However, patient biofluids are a complex milieu of different mHTT protein species, suggesting that absolute quantitation is challenging, and a single, recombinant protein standard may not be sufficient to interpret assay signal as molar mHTT concentration. In this study, we used immunoprecipitation and flow cytometry (IP-FCM) to investigate different factors that influence mHTT detection assay signal. Our results show that HTT protein fragmentation, protein-protein interactions, affinity tag positioning, oligomerization and polyglutamine tract length affect assay signal intensity, indicating that absolute HTT quantitation in heterogeneous biological samples is not possible with current technologies using a single standard protein. We also explore the binding specificity of the MW1 anti-polyglutamine antibody, commonly used in these assays as a mHTT-selective reagent and demonstrate that mHTT binding is preferred but not specific. Furthermore, we find that MW1 depletion is not only incomplete, leaving residual mHTT, but also non-specific, resulting in pull down of some wildtype HTT protein. Based on these observations, we recommend that mHTT detection assays report only relative mHTT quantitation using normalized arbitrary units of assay signal intensity, rather than molar concentrations, in the assessment of central nervous system HTT lowering in ongoing clinical and preclinical studies, and that MW1-depletion not be used a method for quantifying wildtype HTT protein.

Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

Principal component analysis in application to Brillouin microscopy data

Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the microscale mechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information, i.e. each pixel within a 2D/3D Brillouin image is associated with hundreds of points of spectral data. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics.

Anisotropic tumor spheroid remission with binary tumor-microenvironment-on-a-chip

Microphysiological system (MPS) is a powerful tool for the in vitro disease validation platform. It is employed to substantially enhance understanding of tumor and their microenvironments. It aims to assist or replace preclinical studies for validating the efficacy of anti-cancer drugs, precision medicine, and investigating metastatic mechanisms. However, it still faces formidable challenges due to poor and complex usability, low yield, and limited applications for heterogeneous biological samples. Herein, we present a newly developed MPS consisting of a binary tumor-microenvironment-on-a-chip. The system is divided into two independent and separate MPS, each capable of forming a different compartment for the tumor and microenvironment via concurrent processing. These individually formed compartments can be interconnected whenever needed through simple mechanical compression, resulting in a fully integrated tumor-microenvironment-on-a-chip system. This interconnected system enables precise validation of drug efficacy and can be easily separated to retrieve the finished reaction sample for further downstream analysis. In this study, we also propose anisotropic tumor remission by forced convection phenomenon.

Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging

To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability.

Performance Evaluation of Focal Plane Array (FPA)-FTIR and Synchrotron Radiation (SR)-FTIR Microspectroscopy to Classify Rice Components.

The development of biochemical analysis techniques to study heterogeneous biological samples is increasing. These techniques include synchrotron radiation Fourier transform infrared (SR-FTIR) microspectroscopy. This method has been applied to analyze biological tissue with multivariate statistical analysis to classify the components revealed by the spectral data. This study aims to compare the efficiencies of SR-FTIR microspectroscopy and focal plane array (FPA)-FTIR microspectroscopy when classifying rice tissue components. Spectral data were acquired for mapping the same sample areas from both techniques. Principal component analysis and cluster imaging were used to investigate the biochemical variations of the tissue types. The classification was based on the functional groups of pectin, protein, and polysaccharide. Four layers from SR-FTIR microspectroscopy including pericarp, aleurone layer, sub-aleurone layer, and endosperm were classified using cluster imaging, while FPA-FTIR microspectroscopy could classify only three layers of pericarp, aleurone layer, and endosperm. Moreover, SR-FTIR microspectroscopy increased the image contrast of the biochemical distribution in rice tissue more efficiently than FPA-FTIR microspectroscopy. We have demonstrated the capability of the high-resolution synchrotron technique and its ability to clarify small structures in rice tissue. The use of this technique might increase in future studies of tissue characterization.

Abstract 1741: Cancer cell-derived extracellular vesicle mimicking: A parametric study of the physicochemical characteristics of EVs and their influence on cellular uptake in metastasis

Abstract Introduction: Extracellular vesicles (EVs) play a crucial role in disseminating cancer to distant organs, communicating with the tumor microenvironment to prepare the metastatic niche, and through horizontal transfer of oncogenic traits to recipient cells. EV parameters influencing cellular uptake include surface proteins, lipid profile, and physicochemical properties such as size and zeta potential. EVs isolated from cells are heterogeneous populations, which hinder the study of single EV variables and their influence on cellular uptake. Moreover, EV isolation is a lengthy and laborious process with an extremely low yield. Liposomes are synthetic vesicles that share properties with EVs, such as the lipid bilayer and the capability to encapsulate relevant biomolecules. In this study, we used a uveal melanoma (UM) model, a highly liver metastatic tumor, to characterize naturally occurring EVs and model them using liposomes. Methods: We utilized liposomes as a model of EVs to study how the size and zeta potential influence cellular internalization. EVs were isolated from primary UM cells (MP41) using ultracentrifugation (UC) and characterized using dynamic light scattering, nanoparticle tracking analysis, as well as electrophoretic mobility for hydrodynamic size, concentration, and zeta potential, respectively. Liposomes mimicking EVs were produced using the periodic disturbance mixer (nanoprecipitation). The lipid composition was DMPC: CHOL: DHP at different ratios and 0.2% molar concentration of SP-DiIC18(3) as labeling reagent. Liposomes were then dialyzed overnight using a membrane size of 12kDa. Cellular uptake was assessed in immortalized human hepatocytes, representing the liver microenvironment. Results: Using surface response methodology (SRM), we created a model to predict size and zeta potential depending on liposome manufacturing conditions. We produced anionic and neutral liposomes with zeta potential ~-30 mV and 0mV, respectively, and with a size of ~150 nm, similar to EVs derived from MP41 cells in order to evaluate the influence of zeta potential in cellular uptake. Our results showed no significant differences at 0 and 6 hours between anionic and neutral EV-like liposomes. By contrast, the uptake of the anionic liposomes was significantly higher at 24 h compared to neutral ones. Moreover, EV-like liposomes were produced 1000x more concentrated than naturally occurring EVs isolated using UC. Conclusion: In this study, we describe a novel approach using synthetic biology to produce EV-like liposomes as a model to study EV cellular uptake and its role in metastasis. Our data demonstrate that liposomes are a feasible tool to study EV variables individually, thereby addressing the high heterogeneity of biological samples, and producing high liposome yield, thereby helping to accelerate research Citation Format: Rubén Rodrigo López Salazar, Chaymaa Zouggari, Thupten Tsering, Prisca Bustamante Alvarez, Ion Stiharu, Catherine Mounier, Vahe Nerguizian, Julia V. Burnier. Cancer cell-derived extracellular vesicle mimicking: A parametric study of the physicochemical characteristics of EVs and their influence on cellular uptake in metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1741.

Who's Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy.

Simple SummaryNormal-to-malignant transition in human cells is still a poorly understood process, as well as the mechanisms leading to cancer invasion and metastasis. An in-depth characterisation and understanding of the genetic and chemical variations underlying carcinogenesis and cancer progression are paramount for improving diagnosis and design better therapeutic strategies, aiming at a better prognosis for oncology patients. The present study reports the application of spectroscopic methods (micro-Raman and infrared) for identifying specific biomarkers of malignancy in several types of human breast cancer cells. The results thus obtained led to an accurate discrimination between the highly metastatic mammary tumours (triple-negative) and the less aggressive ones (non-triple negative), as well as among triple-negative subtypes.(1) Breast cancer is presently the leading cause of death in women worldwide. This study aims at identifying molecular biomarkers of cancer in human breast cancer cells, in order to differentiate highly aggressive triple-negative from non-triple-negative cancers, as well as distinct triple-negative subtypes, which is currently an unmet clinical need paramount for an improved patient care. (2) Raman and FTIR (Fourier transform infrared) microspectroscopy state-of-the-art techniques were applied, as highly sensitive, specific and non-invasive methods for probing heterogeneous biological samples such as human cells. (3) Particular biochemical features of malignancy were unveiled based on the cells’ vibrational signature, upon principal component analysis of the data. This enabled discrimination between TNBC (triple-negative breast cancer) and non-TNBC, TNBC MSL (mesenchymal stem cell-like) and TNBC BL1 (basal-like 1) and TNBC BL1 highly metastatic and low-metastatic cell lines. This specific differentiation between distinct TNBC subtypes—mesenchymal from basal-like, and basal-like 1 with high-metastatic potential from basal-like 1 with low-metastatic potential—is a pioneer result, of potential high impact in cancer diagnosis and treatment.

Multiplexed sensing of biomolecules with optically detected magnetic resonance of nitrogen-vacancy centers in diamond

In the past decade, a great effort has been devoted to develop new biosensor platforms for the detection of a wide range of analytes. Among the various approaches, magneto-DNA assay platforms have received extended interest for high sensitive and specific detection of targets with a simultaneous manipulation capacity. Here, using nitrogen-vacancy quantum centers in diamond as transducers for magnetic nanotags (MNTs), a hydrogel-based, multiplexed magneto-DNA assay is presented. Near-background-free sensing with diamond-based imaging combined with noninvasive control of chemically robust nanotags renders it a promising platform for applications in medical diagnostics, life science, and pharmaceutical drug research. To demonstrate its potential for practical applications, we employed the sensor platform in the sandwich DNA hybridization process and achieved a limit of detection in the attomolar range with single-base mismatch differentiation.

Design of two Inertial-based microfluidic devices for cancer cell separation from Blood: A serpentine inertial device and an integrated inertial and magnetophoretic device

The separation of cancer cells from a heterogeneous biological sample such as blood plays a vital role in cancer study and future treatments. In this paper, we designed and investigated two microfluidic devices for cancer cell separation, including a serpentine inertial device and an integrated inertial-magnetophoretic device. Firstly, numerical modeling was carried out to study the fluid flow, particles’ trajectories in the inertial device. Then the device was fabricated using soft photolithography and suspension of two types of microparticles with the size of 10 and 15 µm were injected into the microchannel separately to investigate the particles’ trajectories and focusing behavior at different flow rates. Later, MCF7 as a type of cancer cells are mixed with the diluted blood sample, and the mixed cell suspension was introduced into this inertial chip with different flow rates. At 1000 µl /min, the highest purity of 84% and recovery rate of ∼ 89% for cancer cells were attained. Finally, to investigate the integration of the proposed inertial device with magnetic cell separation, a combined inertial and magnetophoretic device was fabricated, and by injecting the blood sample containing the nanoparticle-conjugated MCF7 cells, the purity and recovery rate of isolated cancer cells in the hybrid device improved in comparison to the inertial device to ∼ 92.5% and ∼ 94.5%, respectively.

A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Protocol for preparation of heterogeneous biological samples for 3D electron microscopy: a case study for insects

Modern morphological and structural studies are coming to a new level by incorporating the latest methods of three-dimensional electron microscopy (3D-EM). One of the key problems for the wide usage of these methods is posed by difficulties with sample preparation, since the methods work poorly with heterogeneous (consisting of tissues different in structure and in chemical composition) samples and require expensive equipment and usually much time. We have developed a simple protocol allows preparing heterogeneous biological samples suitable for 3D-EM in a laboratory that has a standard supply of equipment and reagents for electron microscopy. This protocol, combined with focused ion-beam scanning electron microscopy, makes it possible to study 3D ultrastructure of complex biological samples, e.g., whole insect heads, over their entire volume at the cellular and subcellular levels. The protocol provides new opportunities for many areas of study, including connectomics.

Technical Note: CT calibration for proton treatment planning by cross-calibration with proton CT data.

This study explores the possibility of a new method for x-ray computed tomography (CT) calibration by means of cross-calibration with proton CT (pCT) data. The proposed method aims at a more accurate conversion of CT Hounsfield Units (HU) into proton stopping power ratio (SPR) relative to water to be used in proton-therapy treatment planning. X-ray CT scan was acquired on a synthetic anthropomorphic phantom, composed of different tissue equivalent materials (TEMs). A pCT apparatus was instead adopted to obtain a reference three-dimensional distribution of the phantom's SPR values. After rigid registration, the x-ray CT was artificially blurred to the same resolution of pCT. Then a scatter plot showing voxel-by-voxel SPR values as a function of HU was employed to link the two measurements and thus obtaining a cross-calibrated x-ray CT calibration curve. The cross-calibration was tested at treatment planning system and then compared with a conventional calibration based on exactly the same TEMs constituting the anthropomorphic phantom. Cross-calibration provided an accurate SPR mapping, better than by conventional TEMs calibration. The dose distribution of single beams optimized on the reference SPR map was recomputed on cross-calibrated CT, showing, with respect to conventional calibration, minor deviation at the dose fall-off (lower than 1%). The presented data demonstrated that, by means of reference pCT data, a heterogeneous phantom can be used for CT calibration, paving the way to the use of biological samples, with their accurate description of patients' tissues. This overcomes the limitations of conventional CT calibration requiring homogenous samples, only available by synthetic TEMs, which fail in accurately mimicking the properties of biological tissues. Once a heterogeneous biological sample is provided with its corresponding reference SPR maps, a cross-calibration procedure could be adopted by other PT centers, even when not equipped with a pCT system.

An Orthogonal-Pattern Absorption-Mode 2D J -Resolved NMR Spectroscopy for Analyses on Complex Samples

Despite the powerfulness for revealing molecular structures and compositions, conventional 2D J -resolved nuclear magnetic resonance (NMR) experiments are generally exposed to crowded NMR resonances, distorted peak lineshapes, strong coupling artifacts, and inhomogeneous broadening effects, thus rendering its potential applications restricted. Herein, we propose a general NMR method to circumvent these complications and record absorption-mode 2D J -resolved spectra, in which pure chemical shifts and J couplings are presented in two orthogonal dimensions. Thereby, this method facilitates peak assignments at congested spectral regions and implements high-resolution measurements on the desired structural information of J couplings and chemical shifts, even under nonideal magnetic field conditions. Experimental results on samples of a biomolecule and clinical antibiotics demonstrate its suitability and effectiveness for analyzing complex samples exhibiting crowded resonances or strong coupling responses under adverse magnetic field conditions. More importantly, it is available for directly probing heterogeneous biological samples containing intrinsic susceptibility variations and abundant metabolites. In addition, a multiband version is further developed to enhance the spectral sensitivity by collecting signals from multiple spatial slices. This proposed technique is applicable to common commercial liquid NMR spectrometers without special requirements on instrument hardwares and sample pretreatments. Consequently, this study permits composition measurements and multiplet structure analyses on complex samples even in inhomogeneous magnetic fields, thus showing interesting prospects for future chemical and biological applications.

In Depth Analysis of Stretch Function Resulting from Solving the Generalize Fractional-Order Bloch Equations Using Fractional Calculus

In this paper the stretch function resulting from solving the fractional-order Bloch equations using fractional calculus was discussed. This function has promising results to represent diffusion signal decay from MRI images. Conventional analyses of (DWI) measurements resolve the normalized magnetization decay profiles in terms of discrete and mono-exponential components with distinct lifetimes. In complex, heterogeneous biological and biophysical samples such as tissue, multi-exponential decay functions can appear to provide truer representation to normalized magnetization decay profile than the assumption of a mono-exponential decay, but the assumption of multiple discrete components is arbitrary and is often erroneous. Moreover, interactions, between both normalized magnetization and with their environment, can result in complex normalized magnetization decay profiles that represent a continuous distribution of lifetimes. The purpose in this paper is to study different factors that influence the stretch function strength, clarity, and contrast of MRI magnetization signal relaxation by manipulating the anomalous diffusion parameters Δ,δ,Gz,β and μ. of Bloch equations. Through this study, it was found that complex normalized magnetization decay profiles behave like stretch exponential function inside power law. Further developments of this study may be useful in optimizing anomalous diffusion in tissues with neurodegenerative, and ischemic diseases.

Differential Photoelectrochemical Biosensing Using DNA Nanospacers to Modulate Electron Transfer between Metal and Semiconductor Nanoparticles.

As dynamic biorecognition agents such as functional nucleic acids become widely used in biosensing, there is a need for ultrasensitive signal transduction strategies, beyond fluorescence, that are robust and stable for operation in heterogeneous biological samples. Photoelectrochemical readout offers a pathway toward this goal as it offers the simplicity and scalability of electrochemical readout, in addition to compatibility with a broad range of nanomaterials used as labels for signal transduction. Here, a differential photoelectrochemical biosensing approach is reported, in which DNA nanospacers are used to program the response of two sensing channels. The differences in the motional dynamics of DNA probes immobilized on different channels are used to control the interaction between Au and TiO2 nanoparticles positioned at the two ends of the DNA nanospacer to achieve differential signal generation. Depending on the composition of the DNA constructs (fraction of the DNA sequence i.e., double-stranded), the channels can be programmed to produce a signal-on or a signal-off response. Incident photon-to-current conversion efficiency, UV-vis spectroscopy, and flat-band potential measurement indicate that direct transfer of electrons between metallic and semiconductive nanoparticles is responsible for the signal-on response, and incident light absorption and steric hindrance are responsible for the signal-off response. The differential photoelectrochemical signal readout developed here increases the device sensitivity by up to three times compared to a single channel design and demonstrates a limit of detection of 800 aM.

Challenges of Post-measurement Histology for the Dielectric Characterisation of Heterogeneous Biological Tissues.

The dielectric properties of biological tissues are typically measured using the open-ended coaxial probe technique, which is based on the assumption that the tissue sample is homogeneous. Therefore, for heterogeneous tissue samples, additional post-measurement sample processing is conducted. Specifically, post-measurement histological analysis may be performed in order to associate the measured dielectric properties with the tissue types present in a heterogeneous sample. Accurate post-measurement histological analysis enables identification of the constituent tissue types that contributed to the measured dielectric properties, and their relative distributions. There is no standard protocol for conducting post-measurement histological analysis, which leads to high numbers of excluded tissue samples and inconsistencies in the resulting reported data for heterogeneous tissues. To this extent, this study examines the post-measurement histological process and the challenges in associating the acquired dielectric properties with the different tissue types present in heterogeneous samples. The results demonstrate that the histological process inevitably alters the morphology of samples, thus introducing errors in the interpretation of the dielectric properties acquired from heterogeneous biological samples. Notably, sample size was seen to shrink by up to 90% through the histological process, meaning that sensing volume determined from fresh tissues is not directly applicable to histology images.

Vortex trapping and separation of particles in shear thinning fluids

Both enrichment and isolation of target particles from heterogeneous biological or chemical fluid samples are necessary steps in numerous particle-based analyses. We demonstrate, in this work, a vortex-based passive trapping and separation (by size) of particles in the flow of strongly shear thinning xanthan gum solution through a cavity microchannel. Our method utilizes the size-dependent fluid rheology- and inertia-induced lift forces that first align larger particles along the sidewalls of the straight uniform channel section and then drive them toward the microscale vortices developed inside the cavity because of the fluid shear thinning effect. It works effectively at the Reynolds number that is one order of magnitude smaller than the reported inertial vortex trapping for similar-sized particles. Our proposed particle trapping and sorting method in shear thinning fluids will be useful for applications processing medium-volume samples, which may fill the gap between the high-throughput inertial vortex-based passive technique and the usually low-throughput external force-based active techniques.

Model-based correction algorithm for Fourier Transform infrared microscopy measurements of complex tissue-substrate systems

Model-based algorithms have recently attracted much attention for data pre-processing in tissue mapping and imaging by Fourier transform infrared micro-spectroscopy (FTIR). Their versatility, robustness and computational performance enabled the improvement of spectral quality by mitigating the impact of scattering and fringing in FTIR spectra of chemically homogeneous biological systems. However, to date, no comprehensive algorithm has been optimized and automated for large-area FTIR imaging of histologically complex tissue samples. Herein, for the first time, we propose a unique, integrated and fully-automated Multiple Linear Regression Multi-Reference (MLR-MR) method for correcting linear baseline effects due to diffuse scattering, for compensating substrate thickness inhomogeneity and accounting for sample chemical heterogeneity in FTIR images. In particular, the algorithm uses multiple-reference spectra for histologically heterogeneous biological samples. The performance of the procedure was demonstrated for FTIR imaging of chemically complex rat brain frontal cortex tissue samples, mounted onto Ultralene® films. The proposed MLR-MR correction algorithm allows the efficient retrieval of “pure” absorbance spectra and greatly improves the histological fidelity of FTIR imaging data, as compared with the one-reference approach. In addition, the MLR-MR algorithm here presented opens up the possibility for extracting information on substrate thickness variability, thus enabling the indirect evaluation of its topography. As a whole, the MLR-MR procedure can be easily extended to more complex systems for which Mie scattering effects must also be eliminated.

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

IntroductionWe previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.ObjectivesWe aim to analyze a large-scale heterogeneous data compendium generated from our LC–MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.MethodsOur metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case–control paired analysis.ResultsWe generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case–control paired samples.ConclusionsOur study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC–MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.