Biology subjects

Friedrich, J.

Publications and source records attributed to Friedrich, J..

12 recordsLinked to original sources

bioRxiv2026

Avidity-controlled biotherapeutic delivery systems for the treatment of ischemia-reperfusion acute kidney injury

Regulatory T cells (Tregs) are key mediators of immune resolution and tissue repair following acute kidney injury (AKI), yet strategies to selectively enrich Tregs within the renal microenvironment remain limited. Interleukin-2 (IL-2) is essential for Treg survival and function, but systemic IL-2 delivery is constrained by a short half-life, narrow therapeutic window, and risk of off-target activation of cytotoxic T cells. Effective Treg-based immunomodulation may therefore require both localized cytokine signaling and targeted recruitment of precursor immune populations. Here, we present a sequential, biomaterial-based immunomodulatory strategy that integrates immune cell recruitment with sustained cytokine delivery. An injectable granular hydrogel physically encapsulating the chemokine CCL21 was used to initially recruit CD4+ T cells to the injured kidney. Subsequently, supramolecular guest-host interactions were leveraged to deliver IL-2 modified with the hydrophobic "guest" moiety adamantane, enabling prolonged local cytokine release over 28 days. In healthy mice, we observed early CD4+ T cell recruitment, followed by a pronounced increase in Treg frequency, consistent with immune reprogramming driven by our sequential delivery system. In a murine model of bilateral ischemia-reperfusion (BiAKI), this injectable biomaterial induced a local increase in the proportion of FOXP3-expressing Tregs and a concomitant decrease in the levels of AKI markers. This two-step immunomodulatory approach minimizes acute immune activation while promoting long-term enrichment of Tregs within the renal microenvironment. Together, these findings demonstrate that granular hydrogels with supramolecular host capability can orchestrate immune recruitment and differentiation in a spatiotemporally controlled manner, providing a versatile immunomodulatory platform for applicability in kidney disease.

bioengineering↗

bioRxiv2026

GIAnT: a Glutamate Imaging Analysis Toolbox

Recent advances in fluorescent indicators and optical microscopy now enable in vivo synaptic imaging of glutamate, which transmits the majority of signals between neurons in the brain. Extracting fluorescence signals from these recordings is complicated by the minuscule scale and dense clustering of synapses on dendrites, as well as brain motion in behaving animals. Here we present the Glutamate Imaging Analysis Toolbox (GIAnT), a set of automated tools for glutamate imaging data that corrects sample motion, identifies active synapses with super-resolution precision, and extracts synaptic fluorescence signals. Compared to methods designed for cellular imaging, GIAnT reduces motion artifacts, more accurately identifies active synapses, and improves extracted signal quality by reducing contamination from overlapping synapses. By pairing in vivo glutamate imaging with post hoc expansion microscopy, we find that >70% of the putative synapses extracted using GIAnT matched one-to-one with glutamatergic synapses onto the postsynaptic cell. Our results establish GIAnT as an automated and validated pipeline for processing synaptic glutamate imaging data at scale.

neuroscience↗

bioRxiv2025

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of epitranscriptomes and RNA modification.

bioinformatics↗

bioRxiv2024

Direct RNA sequencing (RNA004) allows for improved transcriptome assessment and near real-time tracking of methylation for medical applications

Direct RNA sequencing (DRS) is a nanopore-based technique for analyzing RNA in its native form, promising breakthroughs in diagnostics and biomarker development. Coupled to RNA002 sequencing chemistry, its clinical implementation has been challenging due to low throughput, low accuracy, and lack of large-scale RNA-modification models. In this study, we evaluate the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine using diverse RNA samples from cell lines, synthetic oligos, and human blood. Finally, we present the first clinical application of DRS by confirming the loss of RNA methylation in a patient carrying truncating mutations in the methyltransferase METTL5. Conclusively, the combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. From this perspective, we offer an outlook on the potential suitability of DRS for use in routine diagnostics, the first comprehensive benchmark of human peripheral blood and quality assessments of RNA therapeutics.

bioinformatics↗

bioRxiv2020

Online analysis of microendoscopic 1-photon calcium imaging data streams

In-vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing on GPU hardware. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on two previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. Author summaryCalcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations. Whereas previous methods required the specimen to be stable, e.g. anesthetized or head-fixed, new brain imaging techniques using microendoscopic lenses and miniaturized microscopes have enabled deep brain imaging in freely moving mice. However, the very large background fluctuations, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are computationally expensive due to the complex background model and are typically applied to imaging data after the experiment is complete. Moreover, in some scenarios it is necessary to perform experiments in real-time and closed-loop - analyzing data on-the-fly to guide the next experimental steps or to control feedback -, and this calls for new methods for accurate real-time processing. Here we address both issues by adapting a popular extraction method to operate online and extend it to utilize GPU hardware that enables real time processing. Our algorithms yield similar high-quality results as the original offline approach, but outperform it with regard to computing time and memory requirements. Our results enable faster and scalable analysis, and open the door to new closed-loop experiments in deep brain areas and on freely-moving preparations.

neuroscience↗

bioRxiv2020

VolPy: automated and scalable analysis pipelines for voltage imaging datasets

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-compartment scale, and therefore opens the path to studying sub-threshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios have created a severe bottleneck for analysis of such datasets. Here we present VolPy, the first turn-key, automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features fast motion correction, memory mapping, segmentation, and spike inference, all built on a highly parallelized and computationally efficient framework that optimizes memory and speed. Given the lack of single cell voltage imaging ground truth examples, we introduce a corpus of 24 manually annotated datasets from different preparations and voltage indicators. We benchmark VolPy against this corpus and electrophysiology recordings, demonstrating excellent performance in neuron localization, spike extraction, and scalability.

neuroscience↗

bioRxiv2018

The Hox Transcription Factor Ubx stabilizes Lineage Commitment by Suppressing Cellular Plasticity

During development cells become gradually restricted in their differentiation potential by the repression of alternative cell fates. While we know that the Polycomb complex plays a crucial role in this process, it still remains unclear how alternative fate genes are specifically targeted for silencing in different cell lineages. We address this question by studying Ultrabithorax (Ubx), a multi-lineage transcription factor (TF) of the Hox class, in the mesodermal and neuronal lineages using sorted nuclei of Drosophila embryos and by interfering with Ubx in mesodermal cells that have already initiated differentiation. We find that Ubx is a key regulator of lineage development, as its mesoderm-specific depletion leads to the de-repression of many genes normally expressed in other lineages. Ubx silences expression of alternative fate genes by interacting with and retaining the Polycomb Group (PcG) protein Pleiohomeotic (Pho) at Ubx targeted genomic regions, thereby setting repressive chromatin marks in a lineage-dependent manner. In sum, our study demonstrates that Ubx stabilizes lineage choice by suppressing the multi-potency encoded in the genome in a lineage-specific manner via its interaction with Pho. This mechanism may explain why the Hox code is maintained throughout the lifecycle, since it seems to set a block to transdifferentiation in many adult cells.

developmental biology↗

bioRxiv2018

Bright and photostable chemigenetic indicators for extended in vivo voltage imaging

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, Voltron, that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 minutes of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

neuroscience↗

bioRxiv2018

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

neuroscience↗

bioRxiv2017

OnACID: Online Analysis of Calcium Imaging Data in Real Time

Optical imaging methods using calcium indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing realtime experimental interrogation. Here we introduce OnACID, an Online framework for the Analysis of streaming Calcium Imaging Data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments. We apply our algorithm on two large scale experimental datasets, benchmark its performance on manually annotated data, and show that it outperforms a popular offline approach.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗

bioRxiv2016

Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution.\n\nHere we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover neural activity from data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to \"zoom out\" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.\n\nAuthor SummaryThe voxel rate of imaging systems ultimately sets the limit on the speed of data acquisition. These limits often mean that only a small fraction of the activity of large neuronal populations can be observed at high spatio-temporal resolution. For imaging of very large populations with single cell resolution, temporal resolution is typically sacrificed. Here we propose a multi-scale approach to achieve single cell precision using fast imaging at reduced spatial resolution. In the first phase the spatial location and shape of each neuron is obtained at standard spatial resolution; in the second phase imaging is performed at much lower spatial resolution. We show that we can apply a demixing algorithm to accurately recover each neurons activity from the low-resolution data by exploiting the high-resolution cellular maps estimated in the first imaging phase. Thus by decreasing the spatial resolution in the second phase, we can compress the video data significantly, and potentially acquire images over an order-of-magnitude larger area, or image at significantly higher temporal resolution, with minimal loss in accuracy of the recovered neuronal activity. We evaluate this approach on real data from light-sheet and 2-photon calcium imaging.

neuroscience↗