Introduction: We review recent technological advancements that integrate single-cell transcriptomics with cellular
phenotypes, including cell structure, calcium signaling, and synapses. Single-cell RNA sequencing has
revolutionized the classification of cell types by capturing the heterogeneity of cell transcription. A new
wave of methods combining scRNAseq and biophysical measurements facilitates the connection
between transcriptomic data and cell function, providing insights into synaptic states. We briefly discuss
key factors related to these phenotypic characteristics, such as temporal dynamics, informational
content, and analytical tools. Specific sections focus on integration with cell structure, calcium imaging,
and synapses, emphasizing their complementary roles. We discuss their applications in elucidating
cellular states, refining cell-type classifications, and uncovering functional differences in cellular
subgroups. To demonstrate practical applications and the advantages of these methods, we highlight
their use in tissues with excitable cell types, such as the brain, pancreatic inputs, and the retina. The
potential for combining functional phenotypes with spatial transcriptomics for precise mapping of
cellular appearances is explored. Finally, we address open questions and future perspectives,
emphasizing the need for a broader shift. Access through increased throughput can significantly
contribute to these efforts.
Methods: In a typical workflow, cells are initially imaged using bright- field microscopy, and subsequently each cell
is indepen- dently collected for scRNAseq. However, this requirement for individual cell isolation hinders
throughput and scalabil- ity. Some approaches for cell picking and processing include micropipette
aspiration methods (Camunas-Soler et al. 2020; Cadwell et al. 2016; Tang et al. 2009), capture microdis-
section (Espina et al. 2006), microwells (Gong et al. 2010; Yuan et al. 2018), optofluidic transport
(Berkeley Lights) (Jorgolli et al. 2019), hydrogel-well embedding (Lee et al. 2022), magnetic rafts (Gach et
al. 2011), classic microfluidic valve-based system (Marcus et al. 2006; Wu et al. 2014), and image-based
single-cell isolation (Shomroni et al. 2022). A comprehensive review of these approaches can be found in
Fung et al. (2020). The choice of the optimal system for cell picking depends on the microscopy setup
and the cell type under investigation. Micropipette aspiration methods are well-suited to detach
adherent cells from microplate sur- faces, while nanowells and microfluidic chambers excel at confining
and processing free-floating cells in suspension. Several semi-automated cell-picking systems, inspired by
earlier cell colony pickers, have achieved commercial suc- cess (e.g. CellCellector, Cellenion) (Shomroni et
al. 2022; Nelep and Eberhardt 2018).An elegant alternative to pairwise measurements in the same cell,
is the coupling of droplet-based single-cell tran- scriptomics to image-based screens of organoids. In this
approach organoids are classified based on their morpho- logical profile (morphotype) and subsequently
dissociated to perform scRNAseq in cells from each morphotype (Jain et al. 2023). Applying this
methodology, Liberali and col- leagues screened thousands of intestinal organoids against 301
compounds to identify 15 characteristic organoid phe- notypes by imaging (Lukonin et al. 2020). In this
way, they found a compound that induces a fetal-like regenerative state in enterocytes and measure its
transcriptomic profile. A limitation of this approach is that it cannot establish direct correlations between
morphology and gene expression in each cell but rather only at the population level. However, it is a
powerful approach to identify transcripts enriched in rare cell populations present in morphologically
defined organoids.
Results: Information content
Quantifying the relationship between mRNA abundance
and emerging cellular phenotypes is technically challenging
and remains relatively unexplored. In a study conducted on
human cell lines, various features of global cell state-such
as cell size, cell cycle state, and Ca2+ signaling were meas-
ured alongside single-cell gene expression (Foreman and
Wollman 2020). A linear model incorporating 13 of these
features could explain between 15 and 85% of the measured
variance in gene expression, with a median explanation of 62%. Notably, cell size exhibited the highest
explanatory power, followed by Ca2+ signaling and cell cycle state. Although some Ca2+ features had a
modest effect on the explained variance, most genes exhibited significant correla- tions with at least one
Ca2+ feature, suggesting non-random associations (Foreman and Wollman 2020).
In a subsequent study, information theory was employed to reveal that, conversely, 60% of Ca²+ signaling
dynam- ics could be explained by 83 genes, each contributing up to 17% of the signal. This highlights
substantial redundancy within gene expression networks, hinting that cell state may be effectively
represented by a few latent dimensions (Maltz and Wollman 2022). While cell lines may display consider-
able fluctuations in phenotype and RNA abundance, they are isogenic populations representing
generally homog- enous groups (Emert et al. 2021). Consequently, exploring transcriptome-wide
measurements alongside functional phenotyping in primary cells may shed new light into this question.
Conclusion: Single-cell technologies are revolutionizing the way we approach biology and our ability to measure
cellular diver- sity and heterogeneity. Differences in molecular composi- tion, structure, and morphology
of cells are a critical aspect of cell identity and are connected to its physiological func- tion. Methods to
merge single-cell transcriptomics with other cellular phenotypes such as morphology or electro-
physiological activity enable a more complete understanding of cellular heterogeneity and function,
improving our ability to classify cell types and states.
Neuroscience has pioneered the development of mul- timodal profiling to survey the vast diversity of
neuronal cell types. Among these methods, patch-seq is a powerful approach due to its ability to merge
transcriptome-wide molecular analysis with morphology and electrophysiology. Other fields are
following suit, and multimodal integration of cell physiology and transcriptomics is being used in multiple
tissues. For instance, patch-seq is becoming a popular tool in pancreatic islet research. A caveat of patch-
seq in islet cells is that it has only been performed in dissociated cells, in contrast to in situ and in vivo
studies in neuroscience. Improvements in methods for long-term culture of tissue slices and new
phenotyping tools should enable in situ meas- urements in the future (Speier and Rupnik 2003;
Marciniak et al. 2014; Huang et al. 2011). The development of soft- semiconductor electronics and
microelectrode array systems might enable the recording of tissue-wide electrophysiology (Floch et al.
2022; Li et al. 2021) in parallel to single-cell transcriptomics in multiple tissues. These systems could also
be used to quantify the functional development deep inside 3D organoids. Additionally, given that soft
micro- electronic devices can record the electrical activity of a cell
without perforating the cell membrane, the measurement is non-destructive, and the cell properties can
be followed over time. This could be combined with cytoplasmatic sampling, which makes it possible to
sample the RNA content of the same cell at different time points (Chen et al. 2022b). This approach
could be used to simultaneously track morphologi- cal and transcriptional dynamics of cell populations
during development or under external perturbations.
Currently, the use of approaches that integrate functional phenotyping and single-cell transcriptomics
has remained predominantly limited to specialized laboratories, primarily due to the demanding nature
of obtaining both measure- ments from the same cell. However, new methods to increase throughput,
such as automation or cellular tagging and bar- coding, holds the potential to broaden the accessibility of
these technologies across a wider range of researchers in genomics in physiology. Additionally, progress
in combin- ing functional phenotyping with spatial transcriptomics will offer new possibilities for a
detailed mapping of cell phenotypes in situ and advance our understanding of tissue physiology.
Keywords: structure, phenotype, calcium imaging, cell transcriptomics, excitability function