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Biology subjects

Klock, L.

Publications and source records attributed to Klock, L..

8 recordsLinked to original sources

Regenerative base editing enables deep lineage recording

Reconstructing the lineage histories of individual cells can reveal the dynamics of developmental and disease processes. In engineered recording systems, cells stochastically edit synthetic barcode sequences as they proliferate, creating distinct, heritable edit patterns that can be used to reconstruct the lineage trees relating individual cells in a manner analogous to phylogenetic reconstruction. However, recording depth is often limited by the kinetics of the editing process: the rate of editing declines exponentially over time for an array of independently editable targets, leading to most edits occurring in early generations. Here, we introduce the hypercascade, a regenerative molecular recording system that takes advantage of the predictability of A-to-G base editing to progressively create new target sites over time. The hypercascade packs 4 editable target sites in every 20 bp of sequence, enabling high density information storage. More importantly, the hypercascades regenerative logic leads to an approximately constant rate of mutation accumulation over time. This in turn facilitates reconstruction of deep lineage relationships. We demonstrate this by reconstructing trees spanning 23 days of editing and approximately 17 generations after a single polyclonal engineering step. Finally, simulations show that the hypercascade has the potential to record chromatin state transition dynamics across multiple genomic loci in parallel. The hypercascade thus provides a flexible and broadly useful tool for molecular recording.

bioengineering↗

Engineered protein circuits for cancer therapy

A central challenge in cancer therapy is selectively killing cancer cells while minimizing resistance. Here, we engineer modular protease-based protein circuits that sense mutant RAS, the most frequently mutated oncogene in cancer, and conditionally activate cell death. Delivered transiently as mRNA in lipid nanoparticles (LNPs), circuits selectively eliminated RAS-mutant cancer cells in culture and suppressed aggressive, multifocal RAS-driven liver tumors. Compared to RAS inhibitors, circuits killed cancer cells independently of oncogene addiction, achieved potent cytotoxicity rather than cytostasis, and functioned at low RAS occupancy through catalysis rather than stoichiometric inhibition. Critically, circuits acquired minimal to no resistance under prolonged selection, while remaining potent against prevalent drug-resistance mechanisms including RAS amplification and bypass signaling. Together, these results establish design principles for engineering therapeutic protein circuits and highlight their potential to overcome longstanding limitations of existing therapeutic modalities.

synthetic biology↗

Reconstructing cell histories in space with image-readable base editor recording

Knowing the ancestral states and lineage relationships of individual cells could unravel the dynamic programs underlying development. Engineering cells to actively record information within their own genomic DNA could reveal these histories, but existing recording systems have limited information capacity or disrupt spatial context. Here, we introduce baseMEMOIR, which combines base editing, sequential hybridization imaging, and Bayesian inference to allow reconstruction of high-resolution cell lineage trees and cell state dynamics while preserving spatial organization. BaseMEMOIR stochastically and irreversibly edits engineered dinucleotides to one of three alternative image-readable states. By genomically integrating arrays of editable dinucleotides, we constructed an embryonic stem cell line with 792 bits of recordable, image-readable memory, a 50-fold increase over the state of the art. Simulations showed that this memory size was sufficient for accurate reconstruction of deep lineage trees. Experimentally, baseMEMOIR allowed precise reconstruction of lineage trees 6 or more generations deep in embryonic stem cell colonies. Further, it also allowed inference of ancestral cell states and their quantitative cell state transition rates, all from endpoint images. baseMEMOIR thus provides a scalable framework for reconstructing single cell histories in spatially organized multicellular systems.

synthetic biology↗

Linking cognitive integrity to working memory dynamics in the aging human brain

Aging is accompanied by a decline of multiple cognitive capacities, including working memory: the ability to maintain information online for the flexible control of behavior. Working memory involves stimulus-selective neural activity, persisting after stimulus presentation in widely distributed cortical areas. Here, we unraveled the mechanisms of working memory in healthy older adults and patients with mild cognitive impairment (MCI), a condition associated with increased risk of developing dementia. We studied a sample of 19 older adults diagnosed with MCI and 20 older healthy controls using a combination of model-based behavioral psychophysics, neuropsychological assessment, and magnetoencephalographic (MEG) recordings of brain activity. Twenty-one younger healthy adults were studied with model-based behavioral psychophysics only. All subjects performed a visuo-spatial delayed-match-to-sample working memory task under systematic manipulation of the temporal delay and the spatial distance between successively presented sample and test stimuli. We developed a computational model of the latent dynamics underlying task behavior and fit this to individual behavior. In the model, working memory representations diffused over time, a threshold was applied to produce a match/non-match decision about sample and test locations, and occasional lapses produced random decisions. For the older participants, we related the individual model parameters to a summary measure of individual cognitive integrity obtained from a large neuropsychological test battery, as well as to cortical MEG activity during the delay interval of the working memory task. For all groups, task accuracy decreased with delay duration and sample-test distance. When sample/test distances were small, older adults exhibited larger false alarm rates than younger adults. The behavioral effects were well captured by the model, which explained the age-related differences in terms of a deterioration of the quality of working memory representations, rather than differences in task strategy (i.e., threshold parameter). Task accuracy as well as the parameters governing behavioral stochasticity (diffusion noise and lapse rate combined) were correlated with overall cognitive integrity in the MCI group, but not in the older healthy controls. Individual task accuracy and stochasticity parameters (diffusion noise and lapse rate) were also correlated with stimulus-selective cortical activity during the delay interval, as assessed by decoding of the MEG signals, corroborating their validity as markers of cortical working memory mechanisms. Our findings provide insight into the mechanistic basis of aging-related changes in working memory maintenance and reveal a link between individual working memory dynamics and cognitive integrity in MCI.

neuroscience↗

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and [Formula], proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1 and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the methods sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

neuroscience↗

Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology.\n\nIn this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology.\n\nUsing NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain.\n\nOur results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

neuroscience↗

Reduced Resting-State Connectivity in the Precuneus is correlated with Apathy in Patients with Schizophrenia

A diagnosis of schizophrenia is associated with a heterogeneous psychopathology including positive and negative symptoms. The disconnection hypothesis, an early pathophysiological framework conceptualizes the diversity of symptoms as a result from disconnections in neural networks. In line with this hypothesis, previous neuroimaging studies of patients with schizophrenia reported alterations within the default mode network (DMN), the most prominent network at rest.\n\nAim of the present study was to investigate the functional connectivity during rest in patients with schizophrenia and healthy individuals and explore whether observed functional alterations are related to the psychopathology of patients. Therefore, functional magnetic resonance images at rest were recorded of 35 patients with schizophrenia and 41 healthy individuals. Independent component analysis (ICA) was used to extract resting state networks.\n\nComparing ICA results between groups indicated alterations only within the network of the DMN. More explicitly, reduced connectivity in the precuneus was observed in patients with schizophrenia compared to healthy controls. Connectivity in this area was negatively correlated with the severity of negative symptoms, more specifically with the domain of apathy.\n\nTaken together, the current results provide further evidence for a role DMN alterations might play in schizophrenia and especially in negative symptom such as apathy.

neuroscience↗

New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder.\n\nWhen building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks.\n\nIn this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out.\n\nBy using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

neuroscience↗