Biology subjects

Reinartz, S.

Publications and source records attributed to Reinartz, S..

6 recordsLinked to original sources

bioRxiv2026

Cortical population codes for embedding sensory inputs into the prior context

According to theories of the brain as a predictive network, perceptual decisions result from integrating incoming sensory inputs with prior experience. A neuronal population implementing this form of computation must not only retain information about past events, but also combine this information with current sensory evidence. To examine how this integration occurs, we recorded extracellular activity from both primary sensory cortex and a target frontal region in rats performing stimulus categorization. Psychophysical analysis showed that judgments were history-dependent. Sensory cortex represented the current stimulus largely independently of prior stimuli and failed to account for the history-dependence seen in behavior. By contrast, frontal cortex embedded current input within a representation of prior sensory information through collinearity in coding dimensions. This mechanism - predominantly mediated by fast-spiking neurons - explained trial-to-trial variability in decisions. Our findings argue for distinct roles of cortical regions in predictive processing, and identify a frontal stage where current and prior sensory information converge to inform decisions.

neuroscience↗

bioRxiv2025

Reduced CSF1R expression in myeloid cells has limited impact on chronic lymphocytic leukemia progression

Targeting the colony-stimulating factor 1 receptor (CSF1R) to remove tumor-associated macrophages is being explored as cancer therapy. This strategy may be relevant for chronic lymphocytic leukemia (CLL), which strongly depends on support from myeloid cells. However, it is unclear how CSF1R expression affects the CLL microenvironment and leukemic progression. To examine this question, we created CLL mice with Csf1r haploinsufficiency to investigate changes in myeloid cells, Csf1r expression, and leukemia progression. Reducing Csf1r expression on circulating monocytes did not change the overall numbers of monocytes or macrophages in blood and lymphoid tissues. Mice with lower Csf1r levels had less leukemia during early disease, but this effect faded with disease progression, and their overall survival was similar to controls. Furthermore, reduced Csf1r expression on macrophages did not affect the survival or migration of patient-derived CLL cells. In summary, our results show that while the CSF1R pathway is important for maintaining the myeloid cells that support CLL, simply reducing CSF1R expression has only a limited effect on disease progression. Attempts to target the CSF1R for leukemic therapy might benefit from a stronger depletion of macrophages or the combination with other agents.

cancer biology↗

bioRxiv2025

Systemic MIF facilitates chronic lymphocytic leukemia development independent of its cellular source

Macrophage migration inhibitory factor (MIF) is broadly produced by various cell types, particularly immune cells, and functions as a key modulator of innate and adaptive immunity. Increasing evidence has linked MIF to the pathogenesis of both solid tumors and hematologic malignancies, including chronic lymphocytic leukemia (CLL). We previously showed that the global deletion of Mif in the TCL1 transgenic mouse model for CLL significantly delayed disease development leading to longer overall survival of the knockout mice. In this study, we demonstrated that adaptive transfer of murine CLL cells failed to establish disease in Mif-deficient recipients due to impaired homing of leukemic cells into the spleens, indicating that host-derived Mif is essential for leukemic infiltration and expansion. To identify the most relevant source of Mif in CLL, we generated two CLL mouse strains with B-lymphoid- or myeloid-lineage-specific Mif deletion. In contrast to the global Mif knockout, neither conditional Mif knockout significantly altered CLL progression, illustrating that the cellular source of Mif is less critical than its systemic presence in the tissue environment. Taken together, these in vivo findings indicate that MIF plays a relevant role in CLL pathogenesis, acting independently of its specific cellular origin. HighlightsO_LICLL cells failed to establish disease in Mif-deficient hosts due to impaired tissue homing C_LIO_LIConditional deletion of Mif in B lymphoid- or myeloid cells did not significantly impact CLL progression in vivo C_LIO_LISystemic MIF is critical for CLL pathogenesis, independent of its cellular source C_LI

cancer biology↗

bioRxiv2022

Soluble amyloid-β precursor peptide does not regulate GABAB receptor activity

Amyloid-{beta} precursor protein (APP) regulates neuronal activity through the release of secreted APP (sAPP) acting at cell-surface receptors. APP and sAPP were reported to bind to the extracellular sushi domain 1 (SD1) of GABAB receptors (GBRs). A 17 amino-acid peptide (APP17) derived from APP was sufficient for SD1 binding and shown to mimic the inhibitory effect of sAPP on neurotransmitter release and neuronal activity. The functional effects of APP17 and sAPP were similar to those of the GBR agonist baclofen and blocked by a GBR antagonist. These experiments led to the proposal that sAPP activates GBRs to exert its neuronal effects. However, whether APP17 and sAPP indeed influence classical GBR signaling pathways in heterologous cells was not analyzed. Here, we confirm that APP17 binds to GBRs with nanomolar affinity. However, biochemical and electrophysiological assays indicate that APP17 does not influence GBR activity in heterologous cells. Moreover, we found no evidence for APP17 regulating K+ currents in cultured neurons, neurotransmitter release in brain slices, or neuronal activity in vivo. Our results show that APP17 is not a functional GBR ligand and indicate that sAPP exerts neuronal effects through receptors other than GBRs.

neuroscience↗

bioRxiv2021

The sensory code within sense of time

Sensory experiences are accompanied by the perception of the passage of time; a cell phone vibration, for instance, is sensed as brief or long. The neuronal mechanisms underlying the perception of elapsed time remain unknown1. Recent work agrees on a role for cortical processing networks2,3, however the causal function of sensory cortex in time perception has not yet been specified. We hypothesize that the mechanisms for time perception are embedded within primary sensory cortex and are thus governed by the basic rules of sensory coding. By recording and optogenetically modulating neuronal activity in rat vibrissal somatosensory cortex, we find that the percept of stimulus duration is dilated and compressed by optogenetic excitation and inhibition, respectively, during stimulus delivery. A second set of rats judged the intensity of tactile stimuli; here, optogenetic excitation amplified the intensity percept, demonstrating sensory cortex to be the common gateway to both time and stimulus feature processing. The coding algorithms for sensory features are well established4-10. Guided by these algorithms, we formulated a 3-stage model beginning with the membrane currents evoked by vibrissal and optogenetic drive and culminating in the representation of perceived time; this model successfully replicated rats choices. Our finding that stimulus coding is intrinsic to sense of time disagrees with dedicated pacemaker-accumulator operation models11-13, where sensory input acts only to trigger the onset and offset of the timekeeping process. Time perception is thus as deeply intermeshed within the sensory processing pathway as is the sense of touch itself14,15 and can now be treated through the computational language of sensory coding. The model presented here readily generalizes to humans14,16 and opens up new approaches to understanding the time misperception at the core of numerous neurological conditions17,18.

neuroscience↗

bioRxiv2019

Breaking Medical Data Sharing Boundaries by EmployingArtificial Radiographs

Artificial intelligence (AI) has the potential to change medicine fundamentally. Here, expert knowledge provided by AI can enhance diagnosis by comprehensive and user independent integration of multiple image features. Unfortunately, existing algorithms often stay behind expectations, as databases used for training are usually too small, incomplete, and heterogeneous in quality. Additionally, data protection constitutes a serious obstacle to data sharing. We propose to use generative models (GM) to produce high-resolution artificial radiographs, which are free of personal identifying information. Blinded analyses by computer vision and radiology experts proved the high similarity of artificial and real radiographs. The combination of multiple GM improves the performance of computer vision algorithms and the integration of artificial data into patient data repositories can compensate for underrepresented disease entities. Furthermore, the low computational effort of our method complies with existing IT infrastructure in hospitals and thus facilitates its dissemination. We envision that our approach could lead to scalable databases of anonymous medical images enabling standardized radiomic analyses at multiple sites.

bioinformatics↗