Let’s talk science

June 2022 - present
Identification of viral RNA in single-cell RNA sequencing data, Caltech Pachter lab
Pachter lab, California Institute of Technology (Caltech)

I expanded the open-source (C++) transcriptomic data pre-processing tool kallisto to perform translated alignment of nucleotide sequences to an amino acid reference. To date, this is the only software tool capable of translated alignment while retaining single-cell resolution. I used translated alignment to identify viral RNA in bulk and single-cell RNA sequencing data based on highly conserved motifs, thereby overcoming limitations due to the lack of viral reference genomes. The single-cell resolution allowed the characterization of viral tropism and the prediction of viral presence based on host gene expression. This approach revealed novel viruses whose presence perturbed host gene expression.

Abstract
“More than 300,000 mammalian virus species are estimated to cause disease in humans. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.”

Associated publications:
https://doi.org/10.1101/2023.12.11.571168 (first author)
https://doi.org/10.1101/2023.11.21.568164

June 2022 - present
First clinical trials in humans: CAR T cell therapies for solid tumors
Pachter lab, California Institute of Technology (Caltech)

In collaboration with the Priceman lab at City of Hope National Medical Center, CA USA, we are developing and investigating the efficacy of CAR T cell therapy for solid tumors, including prostate and breast cancer. I analyzed the transcriptomic dataset, including single-cell gene expression and V(D)J immune repertoire sequencing, obtained from the blood, CAR T product, and solid tumor tissue of 12 prostate cancer patients at different time points of CAR T therapy, to reveal significant differences in the immune landscape dynamics. The transcriptomic data analysis identified relevant time points for clonotype expansion, and the existing dataset will be expanded accordingly, as well as to include data from breast cancer patients and cerebrospinal fluid samples.

Associated publications:
https://www.nature.com/articles/s41591-024-02979-8

October 2021 - present
Efficient querying of genomic reference databases with gget
Pachter lab, California Institute of Technology (Caltech)

gget started as a side project to solve a problem I frequently encountered during my work with single-cell RNA sequencing data: how to efficiently access genomic information stored in large public databases and incorporate it into your analysis. After releasing gget and making it freely available in May 2022, it was received with enthusiasm and has since been downloaded > 90,000 times.

We published gget in the journal Bioinformatics. From the abstract:
”A recurring challenge in interpreting genomic data is the assessment of results in the context of existing reference databases. With the increasing number of command line and Python users, there is a need for tools implementing automated, easy programmatic access to curated reference information stored in a diverse collection of large, public genomic databases. gget is a free and open-source command line tool and Python package that enables efficient querying of genomic reference databases, such as Ensembl. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying required for genomic data analysis in a single line of code. The manual and source code are available at https://github.com/pachterlab/gget.”

Associated publications:
https://doi.org/10.1093/bioinformatics/btac836 (first author)
https://doi.org/10.1093/bioinformatics/btae095 (first author)
https://doi.org/10.1101/2023.07.20.549945

September 2020 - December 2023
Brain circuit and song behavior restoration in zebra finches
Pachter lab, California Institute of Technology (Caltech)

I performed the analysis of a large single-cell RNA sequencing dataset obtained from the zebra finch HVC brain region (the first of its kind) before and after chronically perturbing interneurons using targeted expression of tetanus toxin.

Abstract
“Maintaining motor skills is crucial for an animal’s survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.”

Associated publication:
https://doi.org/10.1101/2023.05.17.541057

January 2019 - August 2019
Organ-on-a-Chip Neuronal Model Development, Mimetas BV

April 2018 - December 2018
Lester lab, California Institute of Technology (Caltech)

Development of an optimized fluorescent probe for the visualization of selective serotonin reuptake inhibitors

Applying a combination of genetic, biochemical and pharmacological knowledge, I developed a biosensor with sufficient spatial and temporal resolution to follow SSRIs during and after their incorporation into the cell.

Major depressive disorder (MDD), more commonly known as depression, is a severe mood disorder and one of the most common illnesses worldwide. Depression is frequently treated with selective serotonin reuptake inhibitors (SSRIs), such as Prozac, Lexapro, Paxil, and Zoloft. SSRIs are thought to alleviate symptoms of depression by elevating the amount of extracellular serotonin. You might know that SSRIs typically require 2–6 weeks before a response to treatment is seen. However, the elevation of serotonin levels can be observed within hours after treatment onset. The gap between treatment onset and therapeutic response suggests that the mechanism by which SSRIs exert anti-depressive effects is not only due to an increase in ambient serotonin.

Previously, it was thought that many drugs act exclusively via an extracellular pathway, but alternative thinking now supports the idea that SSRIs might elicit their effect by entering the endoplasmic reticulum (ER) and acting on targets other than extracellular transporters. In collaboration with Dr. Aaron Nichols (Caltech postdoctoral scholar), I developed a biosensor with sufficient spatial and temporal resolution to follow an SSRI during and after its incorporation into the cell to help understand these mechanisms.

The biosensor consists of a periplasmic binding protein (PBP) coupled to a circularly permuted GFP. The PBP is genetically engineered to bind a specific SSRI. Whenever the PBP encounters its target, it undergoes a substantial conformational change - similar to a Venus fly trap catching a fly, First results obtained using the sensor show that SSRIs enter the lumen of the ER at concentrations similar to those detected on the extracellular surface of the plasma membrane. These experiments provide first evidence for the existence and relevance of SSRI drug action within the cell.

My biosensor will contribute to a more detailed understanding of the antidepressant action of SSRIs and hopefully to the development of better treatment for depression, which would generate an immense impact on public health. I presented an abstract of this research at the recent Society for Neuroscience Annual Meeting, where I received invaluable feedback that I am currently incorporating into a scientific publication entitled Genetically encoded biosensors for selective serotonin reuptake inhibitors in collaboration with Aaron.

Associated publications
https://doi.org/10.7554/eLife.74648 (co-first author)
https://doi.org/10.1523/JNEUROSCI.1519-22.2022

February 2017 – August 2017

Ferrari lab, Leiden University Medical Centre (LUMC)

ASSESSING THE EFFECT OF FAMILIAL HEMIPLEGIC MIGRAINE ON SIGNAL TRANSDUCTION IN CORTICAL NETWORKs

I designed computational analysis protocols to describe complex feedback and feed-forward circuits in data generated by non-invasive optogenetic stimulation and partly non-invasive brain recording techniques in freely behaving FHM1 knock-in mice compared to wildtype mice.

July 2016 – August 2016

Taniguchi lab, Max Planck Florida Institute for Neuroscience (MPFI)

SYNAPTOTAGMIN 1 OVEREXPRESSION IN PYRAMIDAL NEURONS – BUILDING A TOOL FOR THE INVESTIGATION OF GABAERGIC INTERNEURONS

I assembled a Tet-On Advanced Inducible Gene Expression System which expresses high levels of a GFP-tagged gene of interest in the presence of the system’s inducer doxycycline, allowing temporally and quantitatively controlled gene expression.

For my first research project, I decided that to understand how the brain works, I first needed to understand its components. I determined that the characterization of neurons was a good starting point. I flew from Leiden to Jupiter (Florida) to join the laboratory of Professor Hiroki Taniguchi at the Max Planck Florida Institute for Neuroscience. His primary interest lays in the connectivity of inhibitory neural circuits, specifically the characterization of GABAergic interneurons. For the first time applying the knowledge acquired during the first year of my bachelor’s in serious research, I developed a gene construct which allowed the inducible overexpression of, amongst others, SYT1 which encodes the membrane-trafficking protein synaptotagmin 1.

I assembled a Tet-On Advanced Inducible Gene Expression System which expresses high levels of the GFP-tagged gene of interest in the presence of the system’s inducer doxycycline. This tool was used to assess the function of synaptotagmin 1 in GABAergic interneurons. More importantly, however, it can be extended to any gene of interest.