Time-dependent diffusion MRI–based microstructural mapping may help diagnose clinically significant prostate cancer, with high accuracy against a histopathologic standard. Background Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value in imaging prostate cancer (PCa) remains unknown. Purp...

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms’ biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

BACKGROUND: Systemic inflammation caused by dysfunctional macrophages is a crucial pathogenetic event in preeclampsia (PE). Trophoblast-derived extracellular vesicles (T-EVs) are potent immune cell signaling modulators in pregnancy. Herein, we aimed to investigate T-EVs’ effect and mechanism on macrophage polarization and its role in PE pathogenesis, which remain unclear. METHODS: Flow cytometry and immunochemistry were used to determine placental macrophage phenotypes. T-EVs were immuno-isolated via placental alkaline phosphatase antibody and identified by transmission electron microscopy and nanoparticle tracking analysis. Quantitative real-time polymerase chain reaction and flow cytometry were used to examine the effects of T-EVs on macrophage polarization, and correlation analysis of T-EVs lipidomics and macrophages transcriptome were performed to explore how T-EVs modulate macrophages. Animal experiments were established to investigate the relationship among PE, T-EVs, and macrophages. RESULTS: Macrophages shift from the M2 to M1 phenotype in the preeclamptic placenta. Also, T-EVs from women with PE (PE-EVs) significantly upregulated M1 gene markers and significantly downregulated CD163 expression in macrophages compared with T-EVs in women with normal pregnancies (NP-EVs). Mechanistically, correlation analysis with T-EVs lipidome and the transcriptome of macrophages treated with PE-EVs or NP-EVs indicated that 37 lipids altered in PE-EVs considerably affected classical inflammatory biological pathways in macrophages. Finally, animal experiments revealed that PE-EVs triggered PE-like symptoms in pregnant mice, which were alleviated after macrophage depletion. CONCLUSIONS: T-EVs from women with PE could promote preeclampsia by inducing macrophage imbalance polarization, signifying a potential novel interventional target for the prevention and management of PE.

MRI-based brain age prediction has been widely used to characterize normal brain development, and deviations from the typical developmental trajectory are indications of brain abnormalities. Age prediction of the fetal brain remains unexplored, although it can be of broad interest to prenatal examination given the limited diagnostic tools available for assessment of the fetal brain. In this work, we built an attention-based deep residual network based on routine clinical T2-weighted MR images of 659 fetal brains, which achieved an overall mean absolute error of 0.767 weeks and R² of 0.920 in fetal brain age prediction. The predictive uncertainty and estimation confidence were simultaneously quantified from the network as markers for detecting fetal brain anomalies using an ensemble method. The novel markers overcame the limitations in conventional brain age estimation and demonstrated promising diagnostic power in differentiating several types of fetal abnormalities, including small head circumference, malformations and ventriculomegaly with the area under the curve of 0.90, 0.90 and 0.67, respectively. In addition, attention maps were derived from the network, which revealed regional features that contributed to fetal age estimation at each gestational stage. The proposed attention-based deep ensembles demonstrated superior performance in fetal brain age estimation and fetal anomaly detection, which has the potential to be translated to prenatal diagnosis in clinical practice.

Gliomas are the most common type of central nervous system tumors in children, and the combination of histological and molecular classification is essential for prognosis and treatment. Here, we proposed a newly developed microstructural mapping technique based on diffusion-time-dependent diffusion MRI t_d-dMRI theory to quantify tumor cell properties and tested these microstructural markers in identifying histological grade and molecular alteration of H3K27. This prospective study included 69 pediatric glioma patients aged 6.14 ± 3.25 years old, who underwent t_d-dMRI with pulsed and oscillating gradient diffusion sequences on a 3T scanner. dMRI data acquired at varying t_ds were fitted into a 2-compartment microstructural model to obtain intracellular fraction (f_in), cell diameter, cellularity, etc. Apparent diffusivity coefficient (ADC) and T1 and T2 relaxation times were also obtained. H&E stained histology was used to validate the estimated microstructural properties. For histological classification of low- and high-grade pediatric gliomas, the cellularity index achieved the highest area under the receiver-operating-curve (AUC) of 0.911 among all markers, while ADC, T1, and T2 showed AUCs of 0.906, 0.885, and 0.886. For molecular classification of H3K27-altered glioma in 39 midline glioma patients, cell diameter showed the highest discriminant power with an AUC of 0.918, and the combination of cell diameter and extracellular diffusivity further improved AUC to 0.929. The t_d-dMRI estimated f_in correlated well with the histological ground truth with r = 0.7. The t_d-dMRI-based microstructural properties outperformed routine MRI measurements in diagnosing pediatric gliomas, and the different microstructural features showed complementary strength in histological and molecular classifications.

Time-dependent diffusion MRI–based microstructural mapping was shown to be an effective method in helping to identify molecular subtypes and predict pathologic complete response to neoadjuvant chemotherapy in participants with breast cancer.