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qPCR in contaminated Swedish sediments

Hello Molecular Biology students!
 
My name is Iria, and I am a former Molecular Biology student currently doing a PhD at LTH. I am reaching out to see if there are any students interested in working with us in the Water Microbiology group during Fall 2025 and/or Spring 2026.
 
In this project, you will use qPCR to investigate contaminated sediments in Sweden. Due to the unregulated wastewater discharge from the pulp and paper industries into the sea and lakes, fiberbanks (anthropogenically caused fibre-rich sediments) have formed all across Sweden. Recently, they have gained attention as potential greenhouse gas (GHG) sources, emitting carbon dioxide and methane when microbes degrade the fibres anaerobically. But how, where, and how much?
 
Two students have already used qPCR to analyze fiberbank bacterial communities, focusing on methane producers, consumers, and antibiotic resistance genes in samples from lake Vättern.
 
This fall, new samples will be taken in the Kramfors area. You will work with us and several other researchers across Sweden to analyze the biology and chemistry of these sediments. You will help understand how methane production varies within different fiberbanks, and how it is linked to microbial communities. You will develop laboratory skills and gain an understanding of environmental microbiology.
 
In addition to myself and the PI of the group (Catherine Paul), there are other 2 PhD students and several Master’s or 10 week project students working together. We meet weekly to talk about all sorts of things related to our science (and sometimes other things too).
 
We are looking for someone who likes microbiology, data, and statistics, and with some qPCR experience. You will receive support throughout this process. You should be curious about biogeochemical cycles and the impact of climate change on GHG emissions.  
 
If this project sounds interesting, please reply by email (iria.feijoo_rey@tvrl.lth.se) explaining why you are interested and if you are looking for 10 or 20-week projects. Please attach a CV in Swedish or English.
 
Iria Feijóo Rey 
PhD Student/Doktorand
 
Water Resources Engineering | Biotechnology & Applied Microbiology
LTH, Faculty of Engineering | Lund University
Visiting address: John Ericssons v 1
April 16, 2025

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Molecular Biology

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Circadian activities and sleep during breeding in Caspian terns (Stenarna, Björns archipelago, Sweden)

Caspian terns (Hydroprogne caspia) are long-lived migratory seabirds and constitute the largest tern species in the world. We are currently looking for a dedicated master’s students to study the circadian activities, foraging, rest, sleep during breeding in the largest and oldest Caspian tern colony in Sweden (Stenarna, Björns archipelago, Baltic sea). You will be based in a small village of Fågelsundet (NE of Uppsala) between the end of April/early May and the mid July 2025 and observe Caspian terns in their breeding colony at Stenarna. The study of breeding activities including parents of sleep and behavior expressed during courtship, incubation and feeding of Caspian tern pairs will occur via a combination of direct observation on the island (with overnight stays in the hide on the Stenarna) and by using the surveillance camera that is accessible remotely. You will also have the possibility to partake in chick ringing sessions. Any student interested in this project is encouraged to contact Susanne Åkesson for more information and to discuss specific ideas for their MSc projects.

Prof Susanne Åkesson, Department of Biology, Evolutionary Ecology and Disease Biology (Room C221)
Email: susanne.akesson@biol.lu.se

April 8, 2025

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Anti-predator behavior and mortality in Caspian terns (Stenarna, Björns archipelago, Sweden)

Caspian terns (Hydroprogne caspia) are long-lived migratory seabirds and constitute the largest tern species in the world. We are currently looking for a dedicated master’s students to study the anti-predator behavior, disturbance and nest failure rates during breeding in the largest and oldest Caspian tern colony in Sweden (Stenarna, Björns archipelago, Baltic Sea). The Master’s student will be based in coastal village of Fågelsundet (Uppsala county) between the end of April/early May and mid July 2025 with the fieldwork team and observe Caspian terns in their breeding grounds on Stenarna. The study of anti-predator behavior, mortality and types of disturbances of breeding Caspian terns will occur via a combination of direct observation on the island (with overnight stays in the hide on the Stenarna), by using the surveillance camera that is accessible remotely and tracking data. The student will also have the possibility to partake in chick ringing sessions on Stenarna. Students interested in this project are encouraged to contact Prof Susanne Åkesson for more information and to discuss specific plans for their MSc projects.

Prof Susanne Åkesson, Department of Ecology, Evolutionary Ecology: susanne.akesson@biol.lu.se

April 8, 2025

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Deep learning of molecular expression of cells from tissue images andspatial omics data

Spatial omics provides unpresidential profiling of tumors which can be useful to predict e.g. patient survival and drug response. The Spatial omics methods are however costly and complex thus currently not applicable in the clinical setting. To combat this issue, we have developed a deep learning algorithm “Image2Count” that learns from spatial omics data to predict molecular marker expression from just low-plex immunofluorescence tissue staining. In this project you will apply our developed method on single cell spatial transcriptomics datasets (CosMx or Xenium) to further validate the performance of Image2Counts. You may also use predicted expression data to model patient outcomes.
Contact: anna.sandstrom_gerdtsson@immun.lth.se

April 5, 2025

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Bioinformatics

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Deep learning to identify prognostic tissue niches in ovarian cancer

While pure sequencing-based methods allow for the identification of prognostic markers that might drive disease progression, recent spatial omics approaches add the additional context of spatial organization of tissue, cell location and molecular expression. This enables the stratification of patients by new spatial markers, for example how much immune cells infiltrate into tumor tissue and relating these findings back to molecular expression. Our group uses the GeoMx technology to manually select regions of interest in tumor tissues, each region containing a few hundred cells, for which we collect bulk count data of proteins and/or transcripts. Using deep learning from images to upfront identify cellular neighbourhoods governing patient outcome would objectively inform selection of regions of interest for detailed spatio-molecular profiling using GeoMx. In this project you will work with multiplex immunofluoresence images from a large cohort of ovarian cancer patients. You will be using published neural network methods, like Naronet or Space-GM, to identify tissue niches which can predict clinical outcomes.
Contact: anna.sandstrom_gerdtsson@immun.lth.se

April 5, 2025

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Bioinformatics

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Paleo-Physiology: The effect of Paleo-mimetic environments on aquatic fern species

Plants transitioned onto land approximately 500 million years ago. Since their transition to land, plants have undergone enormous morphological and molecular adaptation, to fit the terrestrial environment. During their long history on land, the Earth has also experienced many changes to its climate and atmosphere, including multiple mass extinctions, suggesting that the morphological and physiological process optimizations observable in plants today must be the effect of numerous rounds of evolutionary development that have fitted to different environments existing in the Earth‘s history. Ferns were the dominant land plant some 300 million years ago, with many lineages still successful in the present. This project aims to understand their optimizations to a changing atmosphere, by growing a set of aquatic ferns in conditions which mimic the environmental conditions present at various times in Earth’s history, in particular the Mesozoic era. This project will involve establishing an effective system to grow two species of aquatic ferns in the genera Azolla and Salvinia in paleo-mimetic atmosphere, in climate chambers, and investigating their functional response to these conditions at the physiological and molecular level. Results will be compared to extant preliminary data for conifers and angiosperms.

Methods used: Growth rate determination, aqua-culture, microscopy, pigment analysis, determination of cyanobacterial symbiont level by qPCR; with possibility of expanding into global omics approaches if a longer project is designed.

Most suitable background knowledge is a combination of plant ecophysiology and molecular biology. However, it is possible to adapt the project for candidates with more or less ecology or molecular cell biology.

Length of project: Flexible, a project of 30-60 credits can be designed.

Start date: To be discussed

Supervisor(s): Allan Rasmusson, Francois du Toit, Biology, LU

Please contact allan.rasmusson@biol.lu.se or stephanus_francois.du_toit@biol.lu.se

April 3, 2025

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Biology Molecular Biology

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Plant-Trichoderma interactions  

Project suggestions 

Supervisor(s): Allan Rasmusson, Bradley Dotson, Dept. Biology, LU 

 Many fungal species of the genus Trichoderma live in symbiosis with plant roots. The fungus produces substances that stimulate plant growth and immune system but also directly attacking other microorganisms, including pathogens. Therefore, some strains of Trichoderma have been used as biocontrol and biostimulants in agriculture, decreasing the need for agrochemical use. The effect of Trichoderma on plants is strongly dependent on the plant genome, which is involved in a mainly unknown intricate interaction with the fungus, likely involving exchange of several signalling biomolecules.  Plant genes that are essential for biostimulation and biocontrol by fungi can be used in breeding, to make plants that can better benefit from biocontrol and biostimulation. However, we presently do not know what genes these are.  

 One possible class of genes that are essential for positive symbiosis with fungi are plant genes encoding proteins that are needed for the plants to avoid being damaged by the Trichoderma. This fungus attacks other microorganisms by secreting enzymes and peptide antibiotics, including so called peptaibols, where alamethicin is the standard model example. This peptide also lyses plant cells, but cellulase secreted from the Trichoderma induces resistance to the alamethicin by modifying the composition of the plant plasma membrane. This process we have named CIRA, and it is likely important for plant symbiosis with Trichoderma, though direct evidence are lacking. We have isolated CIRA-deficient mutants for a range of Arabidopsis genes, indicating that a so far unknown response chain is active. The different mutants belong to the categories Gene expression, Cell wall modifying, Signalling, Membrane lipid modifying and “Unknown”.  

 We can design differently long projects (30-60 credits) at multiple levels: 

  • Whole plant level comparison of genetics of inbred sugar beet breeding lines as expressed in their biostimulation phenotypes. Includes work with molecular markers, genotyping and phenotyping on soil. 
  • Analysis of particular known Arabidopsis mutants, their (lack of) biostimulation by Trichoderma, and the potential involvement of calcium signalling. This will involve sterile plant growth, fluorescent microscopy and measurements using intracellular fluorescent reporters. 
  •  Identification and analysis of novel CIRA genes by mutant screening. Arabidopsis CIRA mutants are identified by a fluorescent phenotypic change and verified by designing PCR assays and analysing a second mutant allele for the same gene. The genes found are analysed in a first line by bioinformatic database mining (e.g. gene expression patterns, protein localisation, post-translational modification, etc). In a longer project additional analyses can be done. Acquired data will be used in order to assemble a preliminary signalling path. 

 For the sugar beet project, background knowledge on plant physiology, genetics and/or agriculture is suitable. For the Arabidopsis projects, background knowledge on plant physiology, molecular cell biology and molecular genetics is suitable. However, a project can usually be designed in accordance with the background of a candidate. 

 For more information and discussions please contact allan.rasmusson@biol.lu.se or bradley.dotson@biol.lu.se. 

April 3, 2025

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Large scale histology-based image analysis in oncology

Determining the histological structure of tumors is important for understanding spatial tumor biology and to identify the pathological mechanisms underlying cancer. By analyzing tissue architecture, cellular organization, and interactions within the tumor microenvironment, researchers can gain insight into mechanisms behind, for example cancer recurrence, response to treatment and other clinically important features where prediction models are warranted.

The project proposal

The last couple of years have focused on deep learning models for analyzing images derived from biological tissue. There have been models trained to detect different cell type representations in H&E images. One is Hover-Net which both segments and classifies cells into normal epithelial, malignant/dysplastic epithelial, fibroblast, muscle, inflammatory, endothelial or miscellaneous (necrotic, mitotic and cells)1. Another software ConvPath uses a model to define lymphocytes, tumor cells, stroma cells and their regional border2. Detecting and identifying the different cells and regions enables downstream tasks such as investigation of tumour infiltrating lymphocytes (TILs). For example, TILs have proven to predict cancer recurrence3.

The proposed project will explore the different interactions/spatial metrics related to defined cell types, including cell to cell distances, cellular niches and correlate these with clinical parameters such as outcome or previously defined high-risk patient parameters. The goal is to profile and understand the TME better and to find patterns or structures that can be used to stratify patient’s tumor in more detail. The steps would be to identify cells in the tissue by segmentation, then further classify the cells to known cell types. The downstream task involves calculating the distance between cell types based on known metrics such as using the nearest neighbor distance, (Ripley’s) K-function (cross), pair correlation function, neighborhood analysis etc.

Data-set available for the project

The project will have access to HTX staining of duplicate tissues from 650 patients diagnosed with diffuse large B-cell lymphoma (DLBCL).  This is a unique clinical dataset with high potential for translational publications, as well as method development.

Methods

The project will evaluate different workflows for segmenting and classifying cells in DLBCL using python mainly because of the model implementations and Pythons image handling. To tailor the task to lymphoma, different models will be evaluated including HoLy-Net4, Hover-Net with the aim to extract spatial metrics from the full image dataset and to perform data integration with clinical data. Evaluation of the classification model will be performed based on previously available multiplex immunofluorescence data. Downstream statistical tasks can be done in R or Python.

Requirements

We seek a bioinformatic student that is proficient in R, and with basic knowledge of Python. You will have the possibility to deepen your experience in Python and gain hands-on experience on high-throughput image and down-stream data handling as well as data integration.

Principle investigator/supervisor: Sara Ek

Practical supervisor: Daniel Nilsson

Department of Immunotechnology, Lund University

Starting date: flexible, reach out at sara.ek@immun.lth.se to discuss your interest

Length/credits: (30-60 hp) the project can be adapted to fit as either course project or longer combined master thesis projects

References: previous students from the bioinformatic program include Teodor Alling, Mattis Knulst, Daniel Nilsson and Markus Heidrich. Two of previous students are today employed within the group.

 

Literature

  1. Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019). Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical image analysis58, 101563. https://doi.org/10.1016/j.media.2019.101563
  2. Wang, S., Wang, T., Yang, L., Yang, D. M., Fujimoto, J., Yi, F., Luo, X., Yang, Y., Yao, B., Lin, S., Moran, C., Kalhor, N., Weissferdt, A., Minna, J., Xie, Y., Wistuba, I. I., Mao, Y., & Xiao, G. (2019). ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine50, 103–110. https://doi.org/10.1016/j.ebiom.2019.10.033
  3. Corredor, G., Wang, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D. L., Yang, M., Romero, E., Schalper, K. A., Velcheti, V., & Madabhushi, A. (2019). Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research, 25(5), 1526–1534. https://doi.org/10.1158/1078-0432.CCR-18-2013
  4. Naji, H., Sancere, L., Simon, A., Büttner, R., Eich, M. L., Lohneis, P., & Bożek, K. (2024). HoLy-Net: Segmentation of histological images of diffuse large B-cell lymphoma. Computers in biology and medicine, 170, 107978. https://doi.org/10.1016/j.compbiomed.2024.107978
March 27, 2025

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Bioinformatics

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How are solitary bee communities affected by farmland landscape composition?

Pollinating insects, such as wild bees, are declining worldwide, threatening pollination of both wild plants and agricultural crops. The main reason for these declines is intensive farming practices which leads to losses of nesting habitats and flowering food resources [1]. However, all species are not equally affected, with important consequences for which wild plant species or agricultural crops that may suffer pollination losses. This is because certain functional traits (e.g. morphological and life-history traits) make some species more vulnerable to landscape changes than others [2]. Information on which traits make species vulnerable may aid conservation of declining species, by for example, suggesting interventions that match species nesting and foraging preferences or mobility.
This project takes a landscape perspective on bee conservation by investigating how habitat availability and landscape scale complexity influence solitary bee communities.

Potential research questions are, for example:
– How is local bee abundance and diversity affected by local and landscape scale habitat availability?
– How do bee traits interact with habitat availability at local and landscape scales to shape bee communities?
– Is habitat availability (amount) or landscape characteristics most important in moderating bee community composition?

Methods
You will use existing data on cavity nesting bees, flower surveys and land-use data, collected in 54 sites across Scania, combined with data on bee traits. While there are straightforward research questions given by our landscape design, the project provides ample opportunities to analyse data in relation to research ideas developed by you. Depending on the research questions chosen, you will use methods such as GLMs and multivariate statistics. You may also extract additional spatial data on habitat availability using GIS.

Contact: Anna Persson (anna.persson@cec.lu.se) and Henrik Smith (henrik.smith@biol.lu.se)

References:

  1. Potts et al (2010) Global pollinator declines: trends, impacts and drivers. DOI: 10.1016/j.tree.2010.01.007
  2. De Palma (2015) Ecological traits affect the sensitivity of bees to land‐use pressures in European agricultural landscapes. DOI: 10.1111/1365-2664.12524
March 24, 2025

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Biology

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Effect of climate and urbanisation on hatching failure in Great and Blue tits

In recent decades, populations of many wild bird species have declined dramatically worldwide due to many factors associated with urbanisation. Climate change, induced by human activity, influence the population dynamics of wild birds due to its effect on behaviour, physiology, and reproduction. It was shown that in many wild bird species that not just the increasing temperature, but the unpredictable severe weather changes (i.e. immediate temperature drop, or snowing in April) can have a negative impact on nestlings´ growth or fledging success. Unfortunately, our knowledge about how hatching failure, as one of the main determinant of fitness, can change due to climate change, and how this effect is altered by urban environment (eg.: urban heat island effect), is scarce.

Aim of the study: Using our long-term dataset, we will to study, how temperature increase and extreme weather event influence hatching failure in 2 wild bird species (Blue and Great tits).

Questions:

Q1: Did hatching failure increase over the last decade?

Q2: Do the changes in hatching failure differ between urban and rural habitats?

Q3: Do the interaction between climate and urban environment influence hatching failure?

Your tasks:

  • Collect basic breeding data in 2025, following the individual breeding attempt, ringing nestlings and adults, collecting unhatched eggs.
  • Organise the data back till 2013 for your analysis.
  • Collect the weather data for each nestbox from different databases.
  • Carry out the statistical analysis.
  • Write your thesis.

You will learn to:

  • handle and measure birds
  • use brightfield and fluorescent microscope, fluorescent DNA staining of the egg perivitelline layer
  • basic principles of databases and using different statistical tools to analyse your data
  • use GIS for analysing spatial data

Starting date: 1st of April (but sooner is the better). Duration: 45-60 credits

Contact:

Main supervisor: Caroline Isaksson: caroline.isaksson@biol.lu.se

Co-supervisor: Zsófia Tóth: zsofia.toth@biol.lu.se

March 18, 2025

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Biology

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