Biology subjects

Engelmann, S.

Publications and source records attributed to Engelmann, S..

10 recordsLinked to original sources

bioRxiv2026

A framework for peptide identification on commercial nanopore sequencing platforms

Direct single-molecule peptide analysis could in principle enable rapid and sensitive identification of pathogen-derived or disease-associated biomarkers without reliance on mass spectrometry. However, existing nanopore peptide sensing methods are typically constrained by limited throughput and lack of accessibility beyond specialized setups. Here, we present an integrated experimental-computational framework for DNA-linked peptide translocation on a commercially available, high-throughput nanopore sequencing platform, the MinION. Synthetic peptides were covalently bound to oligonucleotides at both termini. The resulting peptide-DNA constructs were then translocated through the CsgG-CsgF pores using a DNA motor protein. Current traces were segmented using the known DNA sequences to extract peptide-associated signal regions. From these segments, we extracted signal features and trained feature-based and deep-learning classifiers to distinguish peptides, balancing interpretability and classification performance. We establish a framework for peptide identification using standard nanopore sequencing hardware. Across a diverse panel of synthetic peptides, our approach resolves single-amino-acid substitutions, maintains performance across independent sequencing runs, and correctly identifies peptides in blind mixtures. Interpretable model analyses connect classifier decisions and common errors to specific signal motifs. By combining commercially available instrumentation with a reproducible experimental and computational workflow, this framework lowers the barrier to nanopore-based proteomics and enables broader adoption across laboratories. It provides a foundation for future developments in amino acid modification detection and sequence analysis.

bioinformatics↗

bioRxiv2024

End-to-end simulation of nanopore sequencing signals with feed-forward transformers

MotivationNanopore sequencing represents a significant advancement in genomics, enabling direct long-read DNA sequencing at the single-molecule level. Accurate simulation of nanopore sequencing signals from nucleotide sequences is crucial for method development and for complementing experimental data. Most existing approaches rely on predefined statistical models, which may not adequately capture the properties of experimental signal data. Furthermore, these simulators were developed for earlier versions of nanopore chemistry, which limits their applicability and adaptability to the latest flow cell data. ResultsTo enhance the quality of artificial signals, we introduce seq2squiggle, a novel transformer-based, non-autoregressive model designed to generate nanopore sequencing signals from nucleotide sequences. Unlike existing simulators that rely on static k-mer models, our approach learns sequential contextual information from the signal data. We benchmark seq2squiggle against state-of-the-art simulators on real experimental R10.4.1 data, evaluating signal similarity, basecalling accuracy, and variant detection rates. Seq2squiggle consistently outperforms existing tools across multiple datasets, demonstrating superior similarity to real data and offering a robust solution for simulating nanopore sequencing signals with the latest flow cell generation. Availability and Implementationseq2squiggle is freely available on GitHub at: github.com/ZKI-PH-ImageAnalysis/seq2squiggle

bioinformatics↗

bioRxiv2024

Non-Degenerate Two-Photon Imaging of Deep Rodent Cortex using Indocyanine Green in the water absorption window

We present a novel approach for deep vascular imaging in rodent cortex at excitation wavelengths susceptible to water absorption using two-photon microscopy with photons of dissimilar wavelengths. We demonstrate that non-degenerate two-photon excitation (ND-2PE) enables imaging in the water absorption window from 1400-1550 nm using two synchronized excitation sources at 1300 nm and 1600 nm that straddle the absorption window. We explore the brightness spectra of indocyanine green (ICG) and assess its suitability for imaging in the water absorption window. Further, we demonstrate in vivo imaging of the rodent cortex vascular structure up to 1.2 mm using ND-2PE. Lastly, a comparative analysis of ND-2PE at 1435 nm and single-wavelength, two-photon imaging at 1300 nm and 1435 nm is presented. Our work extends the excitation range for fluorescent dyes to include water absorption regimes and underscores the feasibility of deep two-photon imaging at these wavelengths.

neuroscience↗

bioRxiv2022

A deep learning approach for improving two-photon vascular imaging speeds

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with a traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to large field of view images and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pre-trained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

bioengineering↗

bioRxiv2022

Complementary Ribo-seq approaches map the translatome and provide a small protein census in the foodborne pathogen Campylobacter jejuni

BackgroundKnowing the molecules encoded by bacterial pathogens and how their expression is regulated is essential to understand how they survive, colonize, and cause disease. RNA-seq technologies that map transcriptomes have revealed a wealth of new transcripts in bacterial pathogens. However, they do not provide direct evidence or coordinates for coding potential. In particular, they miss small proteins ([≤] 50-100 amino acids) translated from small open reading frames (sORFs). However, this still poorly annotated component of bacterial genomes shows emerging roles in bacterial physiology and virulence. ResultsHere, we present an integrated approach based on complementary ribosome profiling (Ribo-seq) techniques to map the "translatome" of Campylobacter jejuni, the most common cause of bacterial gastroenteritis. Besides conventional Ribo-seq, we employed translation initiation site (TIS) profiling to map start codons and reveal internal sORFs. We also developed a Ribo-seq approach for mapping of translation termination sites (TTS), which revealed stop codons not apparent from the reference genome in virulence-associated loci. Our translatome map confirms translation of leaderless ORFs and leader peptides, re-annotates start or stop codons of 35 genes, and reveals isoforms generated by internal start sites. It also adds 42 novel sORFs in diverse contexts to the C. jejuni annotation, such as within small RNAs, in 5' untranslated regions (UTRs), or internal/out-of-frame/antisense in larger ORFs. Using epitope tagging/western blot and mass spectrometry we validated expression of almost 60 annotated and novel sORFs, including cioY, which we show encodes a conserved, 34 amino acid component of the CioAB terminal oxidase. ConclusionsOverall, we provide a blueprint for integrating several Ribo-seq approaches to refine and enrich bacterial annotations.

genomics↗

bioRxiv2022

Interpreting ICT: from Blood Flow to Vascular Network

The inverse correlation time (ICT) is a key quantity in laser speckle contrast imaging (LSCI) measurements. Traditionally, ICT is regarded as a metric of blood flow, such as speed or perfusion. However, we highlight that ICT not only contains important information about blood flow, but also reflects the underlying structure of the vascular network. In the past, ICT has been found to be correlated with vessel diameter. Here, we further report that ICT exhibits a different sensitivity to blood flow depending on vessel orientation. Specifically, ICT is more sensitive to blood flow speed changes in vessels descending from or arising to the tissue surface, compared with those laying parallel to the surface. Those findings shift our understanding of ICT from purely blood flow to a combination of blood flow and vascular network structure. We also develop theories to facilitate the study of vascular networks impact on ICT.

bioengineering↗

bioRxiv2021

Expression levels of long noncoding natural antisense transcripts overlapping the UGT73C6 gene affect rosette size of Arabidopsis thaliana

Natural antisense long noncoding RNAs (lncNATs) are involved in the regulation of gene expression in plants, modulating different relevant developmental processes and responses to various stimuli. We identified and characterized two lncNATs (NAT1UGT73C6 and NAT2UGT73C6, collectively NATsUGT73C6) in Arabidopsis thaliana that are transcribed from a gene overlapping UGT73C6, a member of the UGT73C subfamily of genes encoding UDP-glycosyltransferases (UGTs). Expression of both NATsUGT73C6 is developmentally controlled and occurs independently of the transcription of UGT73C6 in cis. Downregulation of NATsUGT73C6 levels through artificial microRNAs results in a reduction of the rosette area, while constitutive overexpression of NAT1UGT73C6 or NAT2UGT73C6 leads to the opposite phenotype, an increase in rosette size. This activity of NATsUGT73C6 relies on its RNA sequence, and, although modulation of UGT73C6 in cis cannot be excluded, the observed phenotypes are not a consequence of the regulation of UGT73C6 in trans. The NATsUGT73C6 levels were shown to affect cell proliferation and thus individual leaf size. Consistent with this concept, our data suggest that the NATsUGT73C6 modulate the expression levels of key transcription factors involved in regulating leaf growth by modulating cell proliferation. These findings thus reveal an additional regulatory layer on the process of leaf growth.

plant biology↗

bioRxiv2020

A proteogenomics workflow to uncover the world of small proteins in Staphylococcus aureus

Small proteins play diverse and essential roles in bacterial physiology and virulence. Despite their importance, automated genome annotation algorithms still cannot accurately annotate all respective small open reading frames (sORFs), as they usually provide insufficient sequence information for domain and homology searches, tend to be species specific and only a few experimentally validated examples are covered in standard proteomics studies. The accuracy and reliability of genome annotations, particularly for sORFs, can be significantly improved by integrating protein evidence from experimental approaches that enrich for small proteins. Here we present a highly optimized and flexible workflow for bacterial proteogenomics, which covers all steps from (i) creation of protein databases, (ii) database searches, (iii) peptide-to-genome mapping to (iv) result interpretation and whose automated execution is supported by two open source tools (SALT & Pepper). We used the workflow to identify high quality peptide spectrum matches (PSMs) for both annotated and unannotated small proteins ([≤] 100 aa; SP100) in Staphylococcus aureus Newman. Proteins isolated from cells at the exponential and stationary growth phase were digested with different endopeptidases (trypsin, Lys-C, AspN), the resulting peptides fractionated by gel-based and gel-free methods and measured with highly sensitive mass spectrometers. PSMs or sORF predictions from sORFfinder were stringently filtered allowing us to detect 185 soluble SP100, 69 of which were missing in the used genome annotation. Most interestingly, almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids. In addition, phage-related functions were proposed for 30 SP100, based on the localization of their coding sequences in the genome.

genomics↗

bioRxiv2020

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins ([≤] 50 amino acids) more centrally in physiological processes. Yet, the identification of functional small proteins and the systematic genome annotation of their cognate small open reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. Yet, they have difficulties evaluating prokaryotic genomes due to the unique architecture of prokaryotic genomes (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present our new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting smORFs. The unique feature of smORFer is that it uses integrated approach and considers structural features of the genetic sequence along with in-register translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way and dependent on the data available for a particular organism allows using different modules for smORF search.

bioinformatics↗

bioRxiv2019

Dinoroseobacter shibae outer membrane vesicles are enriched for the chromosome dimer resolution site dif

Outer membrane vesicles (OMVs) of Gram-negative bacteria have key roles in pathogenesis. However, little is known about their biogenesis and cargo in marine bacteria. In Dinoroseobacter shibae, a marine member of the Rhodobacteraceae, OMVs were produced throughout exponential growth, and DNA could be detected by fluorescence microscopy inside appr. 65% of vesicles. Single cell analysis using time-lapse microscopy showed that individual cells secreted multiple OMVs, preferentially at the septum during cell division. OMVs were enriched for saturated fatty acids, thus their secretion likely increases the fluidity of the membrane of the releasing cell locally. DNA was isolated from the vesicle lumen and sequenced; it was up to 40fold enriched for the region around the terminus of replication (ter). Within this region, the peak of coverage of vesicle DNA was located at dif, a conserved 28 bp palindromic sequence required for binding of the site specific tyrosine recombinases XerCD which are activated by the divisome protein FtsK immediately prior to septum formation. Some of the most abundant proteins of the vesicle proteome were predicted to be required for direct interaction with peptidoglycan during cell division. Single cell analysis, electron microscopy, proteome and DNA cargo show that constitutive OMV secretion in D. shibae occurs mainly prior to septum formation. The footprint of the FtsK/XerCD molecular machinery which resolves chromosome dimers suggests a novel highly conserved route for incorporation of DNA into OMVs. Clearing the division site from small DNA fragments might be an important function of this type of vesicles.

microbiology↗