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Intestinal organoids are complex three-dimensional structures that mimic the cell-type composition and tissue organization of the intestine by recapitulating the self-organizing ability of cell populations derived from a single intestinal stem cell. Crucial in this process is a first symmetry-breaking event, in which only a fraction of identical cells in a symmetrical sphere differentiate into Paneth cells, which generate the stem-cell niche and lead to asymmetric structures such as the crypts and villi. Here we combine single-cell quantitative genomic and imaging approaches to characterize the development of intestinal organoids from single cells. We show that their development follows a regeneration process that is driven by transient activation of the transcriptional regulator YAP1. Cell-to-cell variability in YAP1, emerging in symmetrical spheres, initiates Notch and DLL1 activation, and drives the symmetry-breaking event and formation of the first Paneth cell. Our findings reveal how single cells exposed to a uniform growth-promoting environment have the intrinsic ability to generate emergent, self-organized behaviour that results in the formation of complex multicellular asymmetric structures. Single-cell-based imaging and sequencing approaches are used to characterize organoid development and the intestinal regeneration process, which is driven by transient activation of YAP1.

Organoids provide an accessible in vitro system to mimic the dynamics of tissue regeneration and development. However, long-term live-imaging of organoids remains challenging. Here we present an experimental and image-processing framework capable of turning long-term light-sheet imaging of intestinal organoids into digital organoids. The framework combines specific imaging optimization combined with data processing via deep learning techniques to segment single organoids, their lumen, cells and nuclei in 3D over long periods of time. By linking lineage trees with corresponding 3D segmentation meshes for each organoid, the extracted information is visualized using a web-based “Digital Organoid Viewer” tool allowing combined understanding of the multivariate and multiscale data. We also show backtracking of cells of interest, providing detailed information about their history within entire organoid contexts. Furthermore, we show cytokinesis failure of regenerative cells and that these cells never reside in the intestinal crypt, hinting at a tissue scale control on cellular fidelity. Live imaging of organoid growth remains a challenge: it requires long-term imaging of several samples simultaneously and dedicated analysis pipelines. Here the authors report an experimental and image processing framework to turn long-term light-sheet imaging of intestinal organoids into digital organoids.

There is emerging evidence that microRNAs (miRNAs) dysregulation is involved in the genesis and the progression of Prostate Cancer (PCa), thus potentially increasing their use in urological clinical practice. This is the first pilot study which utilizes Illumina Deep Sequencing to examine the entire miRNAs spectrum existent in urine exfoliated prostate cells (UEPCs) of PCa patients. A total of 11 male patients with histological diagnosis of PCa were enrolled in the present study. First-catch urine (30 mL) was collected following a prostate massage. Total RNA was extracted from urine and sequenced using an HiSeq2500 System (Illumina). QPCR assay was used to validate the highest NGS results in PCA patients and in age-matched, caucasian men. Remarkably, PCA let-7 family was down-regulated (P < 0.01), compared to the controls. The results of our study support the notion of a relatively high diagnostic value of miRNA family for PCa detection, especially in the let-7 family. The present research confirmed the potential use of miRNAs as non-invasive biomarkers in the diagnosis of PCa, potentially reducing the invasiveness of actual clinical strategy.

Nowadays, analytical techniques are moving towards the development of smart biosensing strategies for the point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum marker for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed to be capable of autonomous calibration and data processing, switching between calibration, and measurement modes: in particular, firstly, a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, thus reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity that is required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing.

Diagnosis of prostate cancer (PCa) is based on prostate biopsy that is performed when prostate specific antigen (PSA) is persistently altered over time and/or abnormal digital rectal examination is found. Serum PSA levels increase in both PCa and benign prostatic hyperplasia, leading to an increased number of unnecessary biopsies. There is an urgent need to unravel PCa-specific molecular signatures. This study aimed at characterizing a panel of circulating micro RNAs (miRNAs) that could distinguish PCa from benign prostatic hyperplasia in a population of age-matched patients with increased PSA levels. Both miRNAs targeting genes involved in PCa onset and miRNAs whose role in PCa has been highlighted in other studies were included. For this purpose, let-7c, let-7e, let-7i, miR-26a-5p, miR-26b-5p, miR-24-3p, miR-23b-3p, miR-27-b-3p, miR-106a-5p, miR-20b-5p, miR-18b-5p, miR-19b-2-5p, miR-363-3p, miR-497, miR-195, miR-25-3p, miR-30c-5p, miR-622, miR-874-3p, miR-346 and miR-940 were assayed through real-time PCR in 64 patients with PCa and compared with 60 patients with benign prostatic hyperplasia. The ability of miRNAs to predict the stage of disease was also analyzed. Let-7c, let-7e, let-7i, miR-26a-5p, miR-26b-5p, miR-18b-5p and miR-25-3p were able to discriminate patients with PCa from those harboring benign prostatic hyperplasia, both presenting altered PSA levels (>3 ng/mL). MiR-25-3p and miR-18b-5p showed the highest sensitivity and specificity to predict PCa, respectively. The combination of these two miRNAs improved the overall sensitivity. A correlation between pathological Gleason score and miRNA expression levels was reported; miR-363-3p, miR-26a-5p, miR-26b-5p, miR-106a-5p, miR-18b-5p, miR-25-3p and let-7i decreased in expression concomitantly with an increase in malignancy. This study confirms serum miRNAs to be reliable candidates for the development of minimally invasive biomarkers for the diagnosis and prognosis of PCa, particularly in those cases where PSA acts as a flawed marker.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

The paper describes the design and implementation of power management circuits for RF energy harvesters suitable for integration in wireless sensor nodes. In particular, we report the power management circuits used to provide the voltage supply of an integrated temperature sensor with analog-to-digital converter. A DC-DC boost converter is used to transfer efficiently the energy harvested from a generic radio-frequency rectifier into a charge reservoir, whereas a linear regulator scales the voltage supply to a suitable value for a sensing and conversion circuit. Implemented in a 65 nm CMOS technology, the power management system achieves a measured overall efficiency of 20%, with an available power of 4.5 μW at the DC-DC converter input. The system can sustain a temperature measurement rate of one sample/s with an RF input power of −28 dBm, making it compatible with the power levels available in generic outdoor environments.

Inflammasome, a large complex of NOD-like receptors (NLRs), drives tumor growth and progression. The present study aimed at exploring the alteration in expression of urinary inflammasome-related microRNAs (miRNAs) in bladder cancer (BC). Our previous report demonstrated the up-regulation of NLRs genes ( , , and ) in urine sediments of patients harboring BC. The expression levels of miRNAs targeting these NLRs (miR-146a-5p, miR-106a-5p, miR-17-5p, miR-223-3p, miR-141-3p, miR-19a-3p, miR-145-5p, miR-185-5p) were assayed in the same patient cohort. Forty-six subjects affected by BC, 28 healthy controls (CTR0) and 31 subjects with histologically confirmed bladder inflammation (CTR1) were recruited. Total RNA was extracted from urine sediment and resulting cDNA was used for amplification by real-time polymerase chain reaction. MiRNA expression levels were evaluated and compared among selected groups. Patients were further stratified according to tumor stage, grade and risk of recurrence and progression. Moreover, non-muscle invasive low-grade and high-grade (HG) BC patients were compared. MiR 141-3p and miR-19a-3p expression decreased in CTR1 with respect to both BC and CTR0. In contrast, miR-146a-5p was up-regulated in BC compared with CTR0. MiR106a-5p, miR17-5p and miR19a-5p were significantly up-regulated in HG, high-risk (HR) and non-muscle invasive HG BC patients, while miR-185-5p was significantly higher in muscle invasive tumors, according to T stage stratification. The increased expression of miRNAs targeting NLRs in HG and HR BC patients is in accordance with the decrease in NLR mRNAs observed in our previous report. These data corroborate the direct role of NLR genes and respective regulatory miRNAs in BC making these inflammasome-related molecules a reliable non-invasive tool for BC diagnosis.

The tree of heaven ( Ailanthus altissima (Mill.) Swingle) is considered to be an early-successional, gap-obligate pioneer species with vigorous height growth, low shade tolerance, early fecundity and large seed production. It is a highly invasive species in many temperate and Mediterranean ecosystems outside its natural range, especially after disturbance. Due to its low shade tolerance, the potential of A. altissima to colonise undisturbed forests is thought to be low. In this study we analysed the potential of juvenile A. altissima to grow and survive in sweet chestnut ( Castanea sativa Mill.) forests in southern Switzerland. We used hemispherical photography to assess the light conditions of 204 individuals of A. altissima (31 % generative, 69 % vegetative) aged between 1 and 7 years (median: 3 years) in six sites. Generative (seed-borne) and vegetative (clonal ramet) offspring of A. altissima are able to grow in light conditions well below the requirements of shade-intolerant tree species such as European larch ( Larix decidua Mill.) and Scots pine ( Pinus sylvestris L.). The relatively low light conditions found to be sufficient for the growth and survival of generative regeneration of A. altissima suggest a higher shade tolerance for this species than previously stated, at least for early regeneration. Consequently, the colonisation frontier of A. altissima should be intensively monitored in both forest openings but also in closed canopy forests in the vicinity of seed-bearing A. altissima .

Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or the spike viral antigen proteins. The immunosensors were realized using disposable screen-printed electrodes modified with nanostructured materials for the immobilization of the antigens. Fast quantitative detection was achieved, with analysis duration being around 1 h. Signal readout was carried out through a smart, compact and battery-powered potentiostat, based on a Wi-Fi protocol and devised for the Internet of Things (IoT) paradigm. This device is used for the acquisition, storage and sharing of clinical data. Outstanding immunosensors’ sensitivity, specificity and accuracy (100%) were assessed, according to the diagnostic guidelines for epidemiological data. The overall performance of the sensing devices, combined with the portability of the IoT-based device, enables their suitability as a high-throughput diagnostic tool. Both of the immunosensors were validated using clinical human serum specimens from SARS-CoV-2 infected patients, provided by IRCCS Ospedale San Raffaele.