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.

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 the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.

Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses

Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.

Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients

BACKGROUND

Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC.

OBJECTIVE

To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients.

DESIGN, SETTING, AND PARTICIPANTS

We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples.

INTERVENTION

Radical or partial nephrectomy on untreated ccRCC patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters.

RESULTS AND LIMITATIONS

We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients.

CONCLUSIONS

We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management.

PATIENT SUMMARY

We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.

Assessment of immune cell profiles among post-menopausal women in the Women's Health Initiative using DNA methylation-based methods

BACKGROUND

Over the past decade, DNA methylation (DNAm)-based deconvolution methods that leverage cell-specific DNAm markers of immune cell types have been developed to provide accurate estimates of the proportions of leukocytes in peripheral blood. Immune cell phenotyping using DNAm markers, termed immunomethylomics or methylation cytometry, offers a solution for determining the body's immune cell landscape that does not require fresh blood and is scalable to large sample sizes. Despite significant advances in DNAm-based deconvolution, references at the population level are needed for clinical and research interpretation of these additional immune layers. Here we aim to provide some references for immune populations in a group of multi-ethnic post-menopausal American women.

RESULTS

We applied DNAm-based deconvolution to a large sample of post-menopausal women enrolled in the Women's Health Initiative (baseline, N = 58) or the ancillary Long Life Study (WHI-LLS, N = 1237) to determine the reference ranges of 58 immune parameters, including proportions and absolute counts for 19 leukocyte subsets and 20 derived cell ratios. Participants were 50-94 years old at the time of blood draw, and N = 898 (69.3%) self-identified as White. Using linear regression models, we observed significant associations between age at blood draw and absolute counts and proportions of naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ memory, neutrophils, and natural killer cells. We also assessed the same immune profiles in a subset of paired longitudinal samples collected 14-18 years apart across N = 52 participants. Our results demonstrate high inter-individual variability in rates of change of leukocyte subsets over this time. And, when conducting paired t tests to test the difference in counts and proportions between the baseline visit and LLS visit, there were significant changes in naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ cells and neutrophils, similar to the results seen when analyzing the association with age in the entire cohort.

CONCLUSIONS

Here, we show that derived cell counts largely reflect the immune profile associated with proportions and that these novel methods replicate the known immune profiles associated with age. Further, we demonstrate the value this methylation cytometry approach can add as a potential application in epidemiological studies.

Impact of immune infiltration signatures on prognosis in endometrial carcinoma is dependent on the underlying molecular subtype

OBJECTIVES

Increased numbers of tumor infiltrating lymphocytes (TIL) in endometrial cancer (EC) are associated with improved survival, but it is unclear how this prognostic significance relates to the underlying EC molecular subtype. In this explorative hypothesis-generating study, we sought to define the immune signatures associated with the molecular subtypes of EC (i.e., POLE-mutated, microsatellite unstable (MSI-high), copy number (CN)-low, and CN-high) and to determine their correlation with patient outcomes.

METHODS

RNA-sequencing and molecular subtype data of 232 primary ECs were obtained from The Cancer Genome Atlas. Deconvolution of bulk gene expression data was performed using single sample Gene Set Enrichment Analysis (ssGSEA) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT). The association of the resultant immune signatures with overall survival was determined across molecular subtypes.

RESULTS

Statistically significant differences in enrichment were identified in 16/30 and 6/23 immune gene sets by ssGSEA and CIBERSORT, respectively. Signature of CD8+ cells in ECs of CN-high molecular subtype was associated with improved overall survival by ssGSEA (p = 0.0108), while CD8 signatures did not appear to be prognostic in MSI-high (p = 0.74) or CN-low EC molecular subtypes (p = 0.793). Of all molecular subtypes, CN-high ECs exhibited the lowest levels of CD8+ T cell infiltration. Consistent with antigen-induced T cell activation and exhaustion, enrichment for immunomodulatory receptors was predominantly observed in ECs of MSI-high and POLE-mutated molecular subtypes.

CONCLUSIONS

Deconvolution of bulk gene expression data can be used to identify populations of immune infiltrated endometrial cancers with improved survival. These data support the existence of unique mechanisms of immune resistance within molecular subgroups of the disease.

Retinex-qDPC: Automatic background-rectified quantitative differential phase contrast imaging

BACKGROUND AND OBJECTIVE

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. The background mismatch reduces the experimental robustness of qDPC and largely limits its applications. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC.

METHODS

In Retinex-qDPC, we replace the data fidelity term of the previous cost function for qDPC inverse problem, by the images' edge features yielding L₂-Retinex-qDPC and L₁-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L₁-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data.

RESULTS

Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L₁-Retinex-qDPC is better than L₂-Retinex and other state-of-the-art qDPC algorithms.

CONCLUSIONS

The Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications.

Deconvolution of 1D NMR spectra: A deep learning-based approach

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.