Trypanosoma cruzi amastigote transcriptome analysis reveals heterogenous populations with replicating and dormant parasites

Trypanosoma cruzi is a protozoan parasite causing Chagas disease, with a complex life cycle involving different stages in insect vectors and mammalian hosts. Amastigotes are an intracellular form that replicates in the cytoplasm of host cells, and recent studies suggested that dormant forms may be contributing to parasite persistence, suggesting cellular heterogeneity among amastigotes. We investigated here if a transcriptomic approach could identify some heterogeneity in intracellular amastigotes and identify a dormant population. We used gene expression data derived from bulk RNA-sequencing of T. cruzi infection of human fibroblasts for deconvolution using CDSeq, which allows to simultaneously estimate amastigote cell-type proportions and cell-type-specific expression profiles. Six amastigote subpopulations were identified, confirming intracellular amastigotes heterogeneity, and one population presented characteristics of non-replicative dormant parasites, based on replication markers and TcRAD51 expression. Transcriptomic approaches appear to be powerful to understand T. cruzi cell differentiation and expansion of these studies could provide further insight on the role different cell types in parasite persistence and Chagas disease pathogenesis.

Application of array-based age prediction models to post-mortem tissue samples

Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18-78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.

A novel computational tool for tracking cancer energy hijacking from immune cells

Recent studies revealed a new biological process that malignant cancer cells hijack mitochondria from nearby T cells, providing another potential mechanism for immune evasion. We further confirmed this process at the single-cell genomic level through MERCI, a novel algorithm for tracking mitochondrial (MT) transfer. Applied to human cancer samples, MERCI identified a new cancer phenotype linked to MT hijacking, correlating with rapid tumour proliferation and poor patient survival. This discovery offers insights into the limitations of current cancer immunotherapies and suggests new therapeutic avenues targeting MT transfer to enhance cancer treatment efficacy.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma

PURPOSE

This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD).

METHODS

Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD.

RESULTS

A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients.

CONCLUSIONS

The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.

Microplastic transport dynamics in surcharging and overflowing manholes

The transport of microplastics within urban water systems remains poorly understood, with little prior research on their behaviour within manhole configurations. This study represents the first to measure and model the transport dynamics of microplastics within circular and square manholes under different hydraulic scenarios. The transport and fate of polyethylene (PE) was quantified and compared to solutes (Rhodamine WT dye) using energy losses, residence time distributions (RTDs), and mixing models within surcharging and overflowing manholes. The bulk mass of solute and PE concentrations followed similar flow paths across all conditions except for 17.3 ± 7.9 % of PE mass that was immobilized in a dead zone above the inlet pipe for manholes with a surcharge to pipe diameter ratio ≥2. Consequently, these microplastics only exit after a significant change in hydraulic regime occurs, causing microplastics to be at risk of being contaminated over a prolonged duration. No significant mixing differences for PE and solutes were found between manhole geometries. The deconvolution method outperformed the ADZ model with goodness of fit (Rt2) values of 0.99 (0.60) and 1.00 (0.89) for PE and solute mixing, respectively. This establishes the deconvolution method as the most accurate and appropriate model to accurately predict microplastic mixing in manholes and urban drainage systems.

DNA methylation-based depiction of the immune microenvironment and immune-associated long non-coding RNAs in oral cavity squamous cell carcinomas

Oral cavity squamous cell carcinoma (OSCC) is a complex and dynamic disease characterized by clinicopathological and molecular heterogeneity. Spatial and temporal heterogeneity of cell subpopulations has been associated with cancer progression and implicated in the prognosis and therapy response. Emerging evidence indicates that aberrant epigenetic profiles in OSCC may foster an immunosuppressive tumor microenvironment by modulating the expression of immune-related long non-coding RNAs (lncRNAs). DNA methylation analysis was performed in 46 matched OSCC and normal adjacent tissue samples using a genome-wide platform (Infinium HumanMethylation450 BeadChip). Reference-based computational deconvolution (MethylCIBERSORT) was applied to infer the immune cell composition of the bulk samples. The expression levels of genes encoding immune markers and differentially methylated lncRNAs were investigated using The Cancer Genome Atlas dataset. OSCC specimens presented distinct immune cell composition, including the enrichment of monocyte lineage cells, natural killer cells, cytotoxic T-lymphocytes, regulatory T-lymphocytes, and neutrophils. In contrast, B-lymphocytes, effector T-lymphocytes, and fibroblasts were diminished in tumor samples. The hypomethylation of three immune-associated lncRNAs (MEG3, MIR155HG, and WFDC21P) at individual CpG sites was confirmed by bisulfite-pyrosequencing. Also, the upregulation of a set of immune markers (FOXP3, GZMB, IL10, IL2RA, TGFB, IFNG, TDO2, IDO1, and HIF1A) was detected. The immune cell composition, immune markers alteration, and dysregulation of immune-associated lncRNAs reinforce the impact of the immune microenvironment in OSCC. These concurrent factors contribute to tumor heterogeneity, suggesting that epi-immunotherapy could be an efficient alternative to treat OSCC.

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

BACKGROUND

Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.

RESULTS

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.

CONCLUSIONS

Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM

BACKGROUND

Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies.

RESULTS

We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data.

CONCLUSION

DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.

Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis

Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics.

In situ forming risperidone implants: Effect of PLGA attributes on product performance

Despite the unique advantages of injectable, long-acting in situ forming implant formulations based on poly(lactide-co-glycolide) (PLGA) and N-Methyl-2-Pyrrolidone (NMP), only six products are commercially available. A better understanding of PLGA will aid in the development of more in situ forming implant innovator and generic products. This article investigates the impact of slight changes in PLGA attributes, i.e., molecular weight (MW), lactide:glycolide (L/G) ratio, blockiness, and end group, on the in vitro and in vivo performance of PLGA-based in situ forming implant formulations. Perseris (risperidone) for extended-release injectable suspension was selected as the reference listed drug (RLD). A previously developed adapter-based USP 2 method was used for the in vitro release testing of various risperidone implant formulations. A rabbit model was used to determine the in vivo pharmacokinetic profiles of the formulations (subcutaneous administration) and deconvolution (Loo-Riegelman method) was conducted to obtain the in vivo release profiles. The results showed that a 5 KDa difference in the MW (19.2, 24.2, 29.2 KDa), a 5% variation in the L/G ratio (85/15, 80/20, 75/25) and the end-cap (acid vs ester) all significantly impacted the formulation behavior both in vitro and in vivo. Higher MW, higher L/G ratio and ester end-cap PLGA all resulted in longer release durations. The formulations prepared with polymers with different blockiness values (within the blockiness range tested) did not show differences in in vitro and in vivo release. An in vitro-in vivo correlation (IVIVC) was not developed due to the different in vitro and in vivo phase separation rates, swelling tendencies and consequent significantly different release profiles. This is the first report evaluating the impact of PLGA property variation (over a narrow range) on the performance of in situ forming implants. The knowledge gained will provide a better understanding of the mechanisms underlying risperidone in situ forming implant performance and will aid the development of future products.

Correlating cryo-super resolution radial fluctuations and dual-axis cryo-scanning transmission electron tomography to bridge the light-electron resolution gap

Visualization of organelles and their interactions with other features in the native cell remains a challenge in modern biology. We have introduced cryo-scanning transmission electron tomography (CSTET), which can access 3D volumes on the scale of 1 micron with a resolution of nanometers, making it ideal for this task. Here we introduce two relevant advances: (a) we demonstrate the utility of multi-color super-resolution radial fluctuation light microscopy under cryogenic conditions (cryo-SRRF), and (b) we extend the use of deconvolution processing for dual-axis CSTET data. We show that cryo-SRRF nanoscopy is able to reach resolutions in the range of 100 nm, using commonly available fluorophores and a conventional widefield microscope for cryo-correlative light-electron microscopy. Such resolution aids in precisely identifying regions of interest before tomographic acquisition and enhances precision in localizing features of interest within the 3D reconstruction. Dual-axis CSTET tilt series data and application of entropy regularized deconvolution during post-processing results in close-to-isotropic resolution in the reconstruction without averaging. The integration of cryo-SRRF with deconvolved dual-axis CSTET provides a versatile workflow for studying unique objects in a cell.

Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography

The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C⁴D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.

ISLET: individual-specific reference panel recovery improves cell-type-specific inference

We propose a statistical framework ISLET to infer individual-specific and cell-type-specific transcriptome reference panels. ISLET models the repeatedly measured bulk gene expression data, to optimize the usage of shared information within each subject. ISLET is the first available method to achieve individual-specific reference estimation in repeated samples. Using simulation studies, we show outstanding performance of ISLET in the reference estimation and downstream cell-type-specific differentially expressed genes testing. We apply ISLET to longitudinal transcriptomes profiled from blood samples in a large observational study of young children and confirm the cell-type-specific gene signatures for pancreatic islet autoantibody. ISLET is available at https://bioconductor.org/packages/ISLET .

CimpleG: finding simple CpG methylation signatures

DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.

NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows

INTRODUCTION

Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling.

OBJECTIVES

An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision.

METHODS

A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool.

RESULTS

For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables.

CONCLUSION

External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.

Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil

BACKGROUND

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use.

METHODS

Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past).

RESULTS

Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains.

CONCLUSION

MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.

Super-resolution imaging of micro- and nanoplastics using confocal Raman with Gaussian surface fitting and deconvolution

Confocal Raman imaging can directly identify and visualise microplastics and even nanoplastics. However, due to diffraction, the excitation laser spot has a size, which defines the image resolution. Consequently, it is difficult to image nanoplastic that is smaller than the diffraction limit. Within the laser spot, fortunately, the excitation energy density behaves an axially transcended distribution, or a 2D Gaussian distribution. By mapping the emission intensity of Raman signal, the imaged nanoplastic pattern is axially transcended as well and can be fitted as a 2D Gaussian surface via deconvolution, to re-construct the Raman image. The image re-construction can intentionally and selectively pick up the weak signal of nanoplastics, average the background noise/the variation of the Raman intensity, smoothen the image surface and re-focus the mapped pattern towards signal enhancement. Using this approach, along with nanoplastics models with known size for validation, real samples are also tested to image microplastics and nanoplastics released from the bushfire-burned face masks and water tanks. Even the bushfire-deviated surface group can be visualised as well, to monitor the different degrees of burning by visualising micro- and nanoplastics. Overall, this approach can effectively image regular shape of micro- and nanoplastics, capture nanoplastics smaller than the diffraction limit, and realise super-resolution imaging via confocal Raman.

Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment

Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular composition of the tumor microenvironment, quantifying the abundance of cell types associated with patients' prognosis and response to therapy. While first-generation deconvolution methods rely on precomputed, transcriptional signatures of a handful of cell types, second-generation methods can be trained with single-cell data to disentangle more fine-grained cell phenotypes and states. These novel approaches can also be applied to spatial transcriptomic data to reveal the spatial organization of tumors. In this review, we describe state-of-the-art deconvolution methods (first-generation, second-generation, and spatial) which can be used to investigate the tumor microenvironment, discussing their strengths and limitations. We conclude with an outlook on the challenges that need to be overcome to unlock the full potential of next-generation deconvolution for oncology and the life sciences.

Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data

BACKGROUND

Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed.

METHODS

We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls.

RESULTS

We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition.

CONCLUSIONS

Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.

Improving the quality of ultrasound images acquired using a therapeutic transducer

To enhance the effectiveness and safety of focused ultrasound (FUS) therapy, ultrasound image-based guidance and treatment monitoring are crucial. However, the use of FUS transducers for both therapy and imaging is impractical due to their low spatial resolution, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). To address this issue, we propose a new method that significantly improve the quality of images obtained by a FUS transducer. The proposed method employs coded excitation to enhance SNR and Wiener deconvolution to solve the problem of low axial resolution resulting from the narrow spectral bandwidth of FUS transducers. Specifically, the method eliminates the impulse response of a FUS transducer from received ultrasound signals using Wiener deconvolution, and pulse compression is performed using a mismatched filter. Simulation and commercial phantom experiments confirmed that the proposed method significantly improves the quality of images acquired by the FUS transducer. The -6 dB axial resolution was improved 1.27 mm to 0.37 mm that was similar to the resolution achieved by the imaging transducer, i.e., 0.33 mm. SNR and CNR also increased from 16.5 dB and 0.69 to 29.1 dB and 3.03, respectively, that were also similar to those by the imaging transducer (27.8 dB and 3.16). Based on the results, we believe that the proposed method has great potential to enhance the clinical utility of FUS transducers in ultrasound image-guided therapy.

Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning

Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. Decomposition analysis is used to deconvolve the EDA into slow and fast varying tonic and phasic activity, respectively. In this study, we used machine learning models to compare the performance of two EDA decomposition algorithms to detect emotions such as amusing, boring, relaxing, and scary. The EDA data considered in this study were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into tonic and phasic components using decomposition methods such as cvxEDA and BayesianEDA. Further, 12 time-domain features were extracted from the phasic component of EDA data. Finally, we applied machine learning algorithms such as logistic regression (LR) and support vector machine (SVM), to evaluate the performance of the decomposition method. Our results imply that the BayesianEDA decomposition method outperforms the cvxEDA. The mean of the first derivative feature discriminated all the considered emotional pairs with high statistical significance (p<0.05). SVM was able to detect emotions better than the LR classifier. We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision, and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively, using BayesianEDA and SVM classifiers. The proposed framework can be utilized to detect emotional states for the early diagnosis of psychological conditions.

Study on reconstruction and analytical method of seawater radioactive gamma spectrum

Gamma detector detection technology based on NaI(Tl) scintillation crystal has become a popular research topic and has been applied in the field of marine radioactive environment automatic monitoring because of its advantages of low power consumption, low cost and strong environmental adaptability. However, insufficient energy resolution of the NaI(Tl) detector and great Compton scattering in the low-energy region caused by the abundance of natural radionuclides in seawater hinder the automatic analysis of radionuclides in seawater. This study adopts the combination of theoretical derivation, simulation experiment, water tank test and seawater field test, establishing an effective and feasible spectrum reconstruction method. The measured spectrum in seawater is regarded as the output signal formed by the convolution of the incident spectrum and the detector response function. The acceleration factor p is introduced to construct the Boosted-WNNLS deconvolution algorithm, which is used to iteratively reconstruct the spectrum. The analysis results of the simulation test, water tank test and field test meet the radionuclide analysis speed and accuracy requirements for the in-situ automatic monitoring of seawater radioactivity. The spectrum reconstruction method in this study converts the physical problem of insufficient detection accuracy of spectrometer in the practical application into a mathematical problem of deconvolution solution, restores the original radiation information in seawater, and improves the resolution of the seawater gamma spectrum.