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More Than Rodents: 8 Alternative Models Used in Single Cell Sequencing Research

More Than Rodents: 8 Alternative Models Used in Single Cell Sequencing Research

Organisms consist of different cell types that function in synchrony. A better understanding of this cellular heterogeneity lets us tackle more complex biological challenges

Model organisms are animals, plants, or other living organisms that are studied to understand biological processes and to serve as a basis for research in fields such as medicine, genetics, and developmental biology.

These organisms are chosen for their simplicity, ease of maintenance and breeding, and availability of genetic and genomic resources. They are used to study complex biological processes that are difficult or impossible to study in humans or other more complex organisms. Traditionally rodents, flies and zebrafish are utilized for biomedical research over the past decades.

Here we show examples of noteworthy single-cell sequencing studies in less frequently used model organisms.

Atopic Dermatitis (AD) is a complex chronic disease that causes inflammation, redness and irritation of the skin. Similar to humans, 3-15% of dogs are affected by AD (1). Recent work from Sparling et al. demonstrated a clear shift in cell populations from samples obtained from healthy to atopic skin (2). They analyzed a total of 153,000 cells and found that proportions of keratinocytes and T cells are increased in AD population. Further analysis of clusters identified the genes that drive pathological progression (2).

Atherosclerosis (AS) is a buildup of fat, cholesterol and other substances on arterial walls. Shi et al. showed that dogs can have progression of AS similar to humans (3). In their recent paper, they used single-cell sequencing obtained from approximately 30,000 cells from AS-prone and AS-resistant arteries to determine the dominant cell types and genes involved in the development of the disease (3).

Chen et al. performed large scale single-cell screening of tissues from various animals including cat, dog, hamster, lizard, goat, rabbit, duck, pigeon, pangolin, tiger and deer to understand viral entry factors for cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (12). Researchers were especially interested in the co-expression of ACE2 and TMPRSS2 in the lungs of different animals. The results of their single-cell analysis showed that SARS-CoV-2 target cells are distributed broadly in the digestive system, respiratory system and urinatory system of the cats (12).

Bats are one of the most diverse mammalian groups and are considered hosts for many different types of viruses that are implicated in recent outbreaks of infectious diseases such as SARS-CoV-2, MERS-CoV. In 2022, Lv et al. performed single nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin (snATAC-seq) on organs obtain from Chinese horseshoe bats (Rhinolophus affinis) to investigate the expression patterns of virus-related receptors. Overall, they analyzed approximately 85,000 nuclei and revealed a distinct expression patterns of various virus receptors (4). Wang et. al. used a different horseshoe bat species (Rhinoplohus sinicus) to perform snRNA-seq on lung tissue. Furthermore, they combined multiple data sets from other groups to better elucidate the cytokine and viral receptor expression across multiple species (5).

Rabbits are valuable model systems especially for antibody research. A recent preprint provides a comprehensive outlook of rabbit embryo development at different stages. The researchers analyzed around 180,000 single cells across multiple stages of embryo development and demonstrated that yolk sac mesothelium in direct contact with hemogenic endothelium and expresses ligand-receptor pairs of key blood maturation genes such as VEGFA and VEGFC. They further confirmed their findings from single-cell analysis with RNAscope in-situ hybridization and immunohistochemistry (6).

Feregrino et. al. sequenced approximately 18,000 single cells from three distinct developmental stages of chicken autopod patterning (7). They identified 23 cell populations with different transcriptional profiles, providing detailed functional genomics resources to study the molecular determinants of limb development. Mantri et. al. combined spatial transcriptomics and scRNA-seq to elucidate the development of chicken hearts. The study analyzed around 22,000 single-cell and 12 spatial gene expression maps which were then integrated using bioinformatic tools. They reconstructed differentiation lineages and unraveled important regulatory programs in cardiac development (8).

Honeybee (Apis mellifera) colonies queen and worker bees. Although the queen and her female worker bees have similar DNA sequences, their roles are quite different. Zhang et. al. used single-cell sequencing to establish transcriptomic atlases of brains from queens and workers and identified 5 major cell types (9). The detailed analysis showed that vitellogenin expression was significantly higher in the brain of queens. Further RNAi experiments to disrupt the expression of vitellogenin in larvae showed that under high nutrition conditions, the larvae with disrupted vitellogenin expression failed to differentiate into queens. This shows the importance of vitellogenin expression in caste differentiation (9).

The mammalian immune system is an extremely heterogeneous system and consists of many different cell types. It is known that the duck immune system is different than mammals and ducks can be a host for zoonotic pathogens. Liang et. al. used single-cell transcriptomics to study initial immune response of ducks to arthropod-borne flaviviral infection (10).

Although single-cell technologies enabled researchers to map the precise expression profile of individual cells in heterogenous cell mixture, they only reflect a snapshot of given time-point. Singleron Biotechnologies developed DynaSCOPE® technology that allows adding the extra “time” dimension to the common single-cell sequencing data. By adding time resolution, deeper analysis of the mechanism of infection by pathogens and studies on dynamic gene expression regulations can be performed.

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Wang and colleagues collected scRNA-seq data from sheep skin and analyzed more than 15,000 cells to study hair follicle development (11). In their data, they identified 19 clusters that reveal the differentiation signals and determined the molecular mechanisms for wool curvature (11).


  1. Hillier A, Griffin CE. The ACVD task force on canine atopic dermatitis (I): incidence and prevalence. Vet Immunol Immunopathol. 2001 Sep 20;81(3-4):147-51. doi: 10.1016/s0165-2427(01)00296-3. PMID: 11553375.
  2. Sparling BA, Moss N, Kaur G, Clark D, Hawkins RD, Drechsler Y. Unique Cell Subpopulations and Disease Progression Markers in Canines with Atopic Dermatitis. J Immunol. 2022 Oct 1;209(7):1379-1388. doi: 10.4049/jimmunol.2200304. Epub 2022 Aug 31. PMID: 36165204.
  3. Shi X, Zhu S, Liu M, Stone SS, Rong Y, Mao K, Xu X, Ma C, Jiang Z, Zha Y, Yan C, Yu X, Wu D, Liu G, Mi J, Zhao J, Li Y, Ding Y, Wang X, Zhang YB, Ji X. Single-Cell RNA-Seq Reveals a Population of Smooth Muscle Cells Responsible for Atherogenesis. Aging Dis. 2022 Dec 1;13(6):1939-1953. doi: 10.14336/AD.2022.0313. PMID: 36465170; PMCID: PMC9662277.
  4. Lv T, Wang X, Yu C, Wang Z, Xiang R, Li L, Yuan Y, Wang Y, Wei X, Yu Y, He X, Zhang L, Deng Q, Wu P, Hou Y, Chen J, Liu C, Wong G, Liu L. A map of bat virus receptors derived from single-cell multiomics. Sci Data. 2022 Jun 14;9(1):336. doi: 10.1038/s41597-022-01447-7. PMID: 35701476; PMCID: PMC9195401.
  5. Wang X, Ding P, Sun C, Wang D, Zhu J, Wu W, Wei Y, Xiang R, Ding X, Luo L, Li M, Zhang W, Jin X, Sun J, Liu H, Chen D. Comparative analysis of single cell lung atlas of bat, cat, tiger, and pangolin. Cell Biol Toxicol. 2022 Sep 28:1–5. doi: 10.1007/s10565-022-09771-9. Epub ahead of print. PMID: 36169743; PMCID: PMC9516514.
  6. Mai-Linh N. Ton, Daniel Keitley, Bart Theeuwes, Carolina Guibentif, Jonas Ahnfelt-Rønne, Thomas Kjærgaard Andreassen, Fernando J. Calero-Nieto, Ivan Imaz-Rosshandler, Blanca Pijuan-Sala, Jennifer Nichols, Èlia Benito-Gutiérrez, John C. Marioni, Berthold Göttgens. bioRxiv 2022.10.06.510971; doi:
  7. Feregrino, C., Sacher, F., Parnas, O. et al. A single-cell transcriptomic atlas of the developing chicken limb. BMC Genomics 20, 401 (2019).
  8. Mantri, M., Scuderi, G.J., Abedini-Nassab, R. et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat Commun 12, 1771 (2021)
  9. Wenxin Zhang, Liangliang Wang, Yinjiao Zhao, Yufei Wang, Chaoyang Chen, Yu Hu, Yuanxiang Zhu, Hao Sun, Ying Cheng, Qinmiao Sun, Jian Zhang, Dahua Chen, Single-cell transcriptomic analysis of honeybee brains identifies vitellogenin as caste differentiation-related factor, iScience,
  10. Liang Y, Ma Y, Zhang Y, Chen Z, Wang Z, Li X, Cui L, Xu L, Liu S, Li H. Single-Cell Analysis of the In Vivo Dynamics of Host Circulating Immune Cells Highlights the Importance of Myeloid Cells in Avian Flaviviral Infection. J Immunol. 2021 Dec 1;207(11):2878-2891. doi: 10.4049/jimmunol.2100116. Epub 2021 Oct 25. PMID: 34697228.
  11. Wang S, Wu T, Sun J, Li Y, Yuan Z, Sun W. Single-Cell Transcriptomics Reveals the Molecular Anatomy of Sheep Hair Follicle Heterogeneity and Wool Curvature. Front Cell Dev Biol. 2021 Dec 21;9:800157. doi: 10.3389/fcell.2021.800157. PMID: 34993204; PMCID: PMC8724054.
  12. Chen, D., Sun, J., Zhu, J. et al. Single cell atlas for 11 non-model mammals, reptiles and birds. Nat Commun 12, 7083 (2021).