Force loading explains spatial sensing of ligands by cells.

Cells can sense the density and distribution of extracellular matrix (ECM) molecules by means of individual integrin proteins and larger, integrin-containing adhesion complexes within the cell membrane. This spatial sensing drives cellular activity in a variety of normal and pathological contexts. Previous studies of cells on rigid glass surfaces have shown that spatial sensing of ECM ligands takes place at the nanometre scale, with integrin clustering and subsequent formation of focal adhesions impaired when single integrin-ligand bonds are separated by more than a few tens of nanometres. It has thus been suggested that a crosslinking 'adaptor' protein of this size might connect integrins to the actin cytoskeleton, acting as a molecular ruler that senses ligand spacing directly. Here, we develop gels whose rigidity and nanometre-scale distribution of ECM ligands can be controlled and altered. We find that increasing the spacing between ligands promotes the growth of focal adhesions on low-rigidity substrates, but leads to adhesion collapse on more-rigid substrates. Furthermore, disordering the ligand distribution drastically increases adhesion growth, but reduces the rigidity threshold for adhesion collapse. The growth and collapse of focal adhesions are mirrored by, respectively, the nuclear or cytosolic localization of the transcriptional regulator protein YAP. We explain these findings not through direct sensing of ligand spacing, but by using an expanded computational molecular-clutch model, in which individual integrin-ECM bonds-the molecular clutches-respond to force loading by recruiting extra integrins, up to a maximum value. This generates more clutches, redistributing the overall force among them, and reducing the force loading per clutch. At high rigidity and high ligand spacing, maximum recruitment is reached, preventing further force redistribution and leading to adhesion collapse. Measurements of cellular traction forces and actin flow speeds support our model. Our results provide a general framework for how cells sense spatial and physical information at the nanoscale, precisely tuning the range of conditions at which they form adhesions and activate transcriptional regulation.

Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury.

OBJECTIVES: This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI). METHODS: Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers. RESULTS: Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%). CONCLUSIONS: A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.

Establishing an acquisition and processing protocol for resting state networks with a 1.5 T scanner: A case series in a middle-income country.

OBJECTIVE: The aim of this study was to characterize the capability of detection of the resting state networks (RSNs) with functional magnetic resonance imaging (fMRI) in healthy subjects using a 1.5T scanner in a middle-income country. MATERIALS AND METHODS: Ten subjects underwent a complete blood-oxygen-level dependent imaging (BOLD) acquisition on a 1.5T scanner. For the imaging analysis, we used the spatial independent component analysis (sICA). We designed a computer tool for 1.5 T (or above) scanners for imaging processing. We used it to separate and delineate the different components of the RSNs of the BOLD signal. The sICA was also used to differentiate the RSNs from noise artifact generated by breathing and cardiac cycles. RESULTS: For each subject, 20 independent components (IC) were computed from the sICA (a total of 200 ICs). From these ICs, a spatial pattern consistent with RSNs was identified in 161 (80.5%). From the 161, 131 (65.5%) were fit for study. The networks that were found in all subjects were: the default mode network, the right executive control network, the medial visual network, and the cerebellar network. In 90% of the subjects, the left executive control network and the sensory/motor network were observed. The occipital visual network was present in 80% of the subjects. In 39 (19.5%) of the images, no any neural network was identified. CONCLUSIONS: Reproduction and differentiation of the most representative RSNs was achieved using a 1.5T scanner acquisitions and sICA processing of BOLD imaging in healthy subjects.