# Special Issue “Single-cell analysis: Epistemological inquiries”

Caroline Angleraux, Matteo Mossio

► To cite this version:

Caroline Angleraux, Matteo Mossio. Special Issue “Single-cell analysis: Epistemological inquiries”. Organisms. Journal of Biological Sciences, 2023, 6 (2), pp.5-9. 10.13133/2532-5876/18311. hal-04335128

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Submitted on 18 Dec 2023

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cell analysis contribute to provide a more adequate
explanation of biological phenomena?

In the study of multicellular systems, sequencing a
cell sample usually implies a global genetic knowledge
of the tissue, which hides the intrinsic heterogeneity
of molecular profiles within the sample. The rationale
behind the use of single-cell analysis might be described
as follows (Qian & Bao 2019):

- Understanding multicellular organization requires understanding the different functions exerted by different types of cells, assembled in tissues and organs;

- Cell types can be identified by a certain
molecular profile. By hypothesis, all the cells belonging
to the same type share the same molecular profile;

- Single-cell analysis allows biologists to detect different molecular profiles within a single tissue, and to distinguish between different cell types, beyond global “means” established over populations of cells.

In the case of unicellular systems, see for instance
(Ku & Sebé-Pedros 2019), single-cell analysis is used
within an evolutionary approach:

- Understanding how unicellular organisms interact with their environment requires understanding how they adapt or are more specified (for studying symbiosis, ecological or evolutionary processes);

- Types can be identified and clustered into phyla
depending on the molecular profiles;

- Single-cell analysis allows biologists to track
the specification within different unicellular phyla, by
detecting different molecular patterns.

Overall, this rationale relies on a twofold background
presupposition, according to which:

- Molecular characteristics are more reliable
than other criteria (like metabolic behavior) to typify
cells;

- The functional role of cells is subtended by
a combination of molecular characteristics, i.e. their
"molecular profile".

Such a presupposition is theoretically loaded, and
raises an epistemological problem that might be broken
down into three related issues.

(1) Single-cell analysis is claimed to provide a global and even holistic approach, insofar as it can combine datasets obtained through various omics technologies, and referring to different biological objects such as mRNA, DNA, ribosomal RNA, etc. (Anam et al. 2019). Yet, the question is how single-cell analysis would realize

a more comprehensive view of living beings. Indeed,
the very idea according to which the molecular profile
of a cell subtends its functional role is consistent with a
reductionist approach to biological phenomena. And it
might be argued that looking at the various molecular
characteristics of a cell does not inform as such about its
dynamic organization. Understanding how a cell works
would imply a wider vision that does not focus on the
molecular level alone. If so, then single-cell analysis
provides data that are not sufficient to make sense of
cell functions.

(2) The presupposition that the molecular profile
is relevant (and sufficient) to categorize cells into
types seems to overlook the processual dimension of
biological phenomena. Cells are dynamic entities that
undergo a life cycle, during which their molecular
profile changes over ontogenetic time. Cells are
plastic, some can dedifferentiate or transdifferentiate;
ontogeny is quite reversible. In contrast, single-cell
analysis provides stable data, that describe a biological
mapping at a given moment (a "snapshot"), with a
given set of spatial interactions. Single-cell analysis (for
now) is only able to take snapshots of cells' life cycles,
which means that it cannot produce any description of
individual trajectories and it cannot determine whether
a certain snapshot is representative of a certain cell
type (Trapnell et al. 2014). This snapshot has to be
put into perspective and compared either with other
snapshots of the same biological process at a different
time, or stated as the representative of a given cell type,
based on previous knowledge. For now, the description
of developmental or transitional dynamics in cells
relies on a pseudo-time derived from a comparison
of quantitative measurements between proximate
moments in different cells in order to infer the states
that precede or follow each other.

These limitations raise several questions: (a)
To what extent does the pseudo-time account
for the developmental time of a living organism?
(b) By relying on molecular patterns only, how
distinguishing between two different cell types, on
the one hand, and the same cell at two different
moments, on the other hand? (c) How determining
which snapshot better characterizes a cell type?
As a consequence of the dynamic nature of living
processes, a (theoretical) choice should be made
regarding what "moment" in the cell's life cycle is
the relevant one to determine its type.
(3) More generally, single-cell analysis claims to be data-driven only. In fact, it seems to rely rather on epistemological choices, which are not always explicit (Leonelli 2019). There is nowadays an increasing acknowledgement of the “heterogeneity” of data across individual cells. Cells that supposedly belong to the same type and perform the same function (because they are in the same tissue for instance) do not exhibit the same molecular profile. In general, molecular data exhibit a continuum among the various categories of cell rather than sharp discontinuities. Instead of finding similarities, single-cell analysis finds differences. Such heterogeneity pushes biologists to multiply categories (“rare” cell types), which risks to be a useless process that would result in obtaining as many types as individual cells.

Therefore, single-cell analysis requires making epistemological choices while structuring categories. It is what happens when bio-analysts define clusters by selecting a set of features, and measure to what extent each cell possess such features. As Gross (this issue) puts it, “membership is based on overall similarity, that is, the degree to which objects share a set of properties”. Clustering requires to make a choice about what features are considered as relevant, and how the “distance” or “score” is measured. Depending on these choices, different categories emerge. Single-cell analysis is not (and cannot be) entirely data-driven: categorization does not emerge from data themselves. Categories depend on choices, which in turn depend on previous knowledge about cell types, background theories and assumptions.

The issue of identifying and making explicit epistemological choices also raises a number of questions: (a) What criteria should determine the features by which categories and clusters are elaborated? And how do these criteria promote different research directions? (b) How does background (structural, functional and genealogical) knowledge affect the elaboration of cells categories? (c) How do background epistemological choices impact information sharing and communication? A research team produces databases that can be hard to understand by a different team. The way to classify and identify nomenclatures (which exacerbates the variable number and the tendency towards multiplications of categories, as we mentioned before) may complexify the adequation between different databases. It also questions, in another way,

the ability to reproduce results. In this sense, making explicit epistemological choices is an absolute necessity for securing disclosable data.

In a word, the focus of single-cell analysis on cells molecular profile raises a number of questions about reductionism, cell dynamics and implicit epistemological choices. More generally, single-cell analysis can be critically examined in terms of the characterization of cells and different biological processes that it puts forward, as well as the criteria for biological identity that it adopts. During its history, biology has oscillated between structural, functional and genealogical criteria, and the debate about their relation is a never-ending one. The epistemological enquiries about single-cell analysis should also be located within this larger and fascinating debate.

The four contributions to this special issue, authored by biologists and philosophers alike, examine the above questions from different, and yet complementary perspectives.

In his contribution, Fridolin Gross examines how single-cell analysis impacts the very concept of cell type. He emphasizes the tension existing between the idea of using single-cell analysis to elaborate more solid cell types and the recognition of huge spatial and temporal cellular heterogeneity. He describes what might possibly be labelled a “molecular pheneticist account” to cell types, and focuses on (and questions) the claim that such account might be theory-free. Gross shows that fundamental steps in single-cell analysis (as dimensionality reduction and clustering methods) do require to make choices (based on theories or at least on background knowledge) about the number of dimensions and the parameter values. Above all, clustering methods are “importantly driven by the concern to reproduce previously accepted cell type classifications”.

Gross concludes by claiming “it seems inappropriate to refer to them as ‘theory-free’ or purely data-driven as this would ignore the clearly theory-guided process of method selection”. Gross generalizes his argument. According to him, thinking that, in principle, the more data are added, the more they would converge in creating stable categories in a theory-free manner is delusional. Even more generally, Gross mentions the fact that focusing on “structures” is not a straightforward choice, because of the everlasting tension with functional and genealogical criteria. So Gross asks: “Why then should biologists focus
so much on a theory-free classification approach if
that approach misses the central goal that cell type
classifications are meant to achieve?".

Racine and Paldi also underscore that single-cell
analysis is supposed to contribute to our understanding
of cells identity and differentiation. Classifications
based on origins vs. similarities have always co-existed,
but the more recent idea is that "cells of the same type
must express the same genes and can be identified on
the basis of the transcriptional regulator (transcription
factors) they express".

As Gross, they emphasize the strong heterogeneity
in gene expression, and in the resulting molecular
profile. So, while cells belonging to different tissues or
organs tend to exhibit distinguishable gene expression
patterns, the same is not true for supposedly different
cells belonging to the same tissue, for instance.
According to them, the continuous nature of gene
expression in cell population makes that "If one picks
up randomly a cell from the population, there are
good chances that it is impossible to say on the basis
of its gene expression pattern to which type it belongs".
Again, clustering methods, no matter how powerful they
are, do not produce types by themselves, but depend on
several background choices made by the biologist (about
p-values, thresholds, filters, the presumed number of
clusters one expects, etc.).

Racine and Paldi also suggest that the more our
analysis is fine-grained, the more we find molecular
differences. We find “rare cell types”, and “cell states”
which, according to the authors, do not change anything
to the initial problem. The identity of cells is contextual
and dynamic, and any search by single-cell analysis
should rely on a definition elaborated beforehand. This
means in particular deciding what counts as relevant
stability in cell life cycles, which are profoundly variable
and dynamic. We should not forget that “stability” itself
is a scale-dependent notion, with respect to which a
decision also has to be taken. The Authors conclude
by calling for “a new interpretation framework based
on solid theoretical ground”, possibly centered on the
organicist tradition.

Heams' article agrees with the previous ones about
the fact that the questions raised by single-cell analysis
are not just technical, but profoundly epistemological
and theoretical. Heams underscores that single-
cell analysis is mainly used within the Genetic
Determinism Paradigm, according to which (among

other things) similarities and differences among cells
should unambiguously correspond to similarity and
differences in their gene expression and in their overall
molecular profile. Yet, single-cell analysis has shown
for 20 years now that even a population of clonal cell
shows very heterogeneous gene expression, to the
point that the idea of stochastic gene expression was
proposed, and it constitutes now a solid hypothesis in
molecular biology. Single-cell analysis, in this sense,
contributed not to find stability and categories, but to
shake the very foundations of GDP. Heams discusses
the various ways of interpreting unpredictable
variability, ranging from the more conservative and
GDP-related to the more original and alternative
one, which Heams calls the “probabilistic alternative
framework (PAF)”. PAF claims that gene expression
is fundamentally stochastic and incompatible with
GDP. Heams discusses the strength and weaknesses
of PAF, and in particular the extent to which it is at
odds with some of the theoretical pillar of evolutionary
theory, i.e. the necessity of cooperative behavior. On
this crucial point, Heams argues that PAF does not
exclude cooperative behavior, but that this very notion
should be reconceptualized within a probabilistic
framework. Heams goes farther in discussing how
both GDP and PAF are challenged by the discovery
that cells constituting a multicellular system are
not genetically homogeneous, although he argues
that the challenge is not the same. GDP “is affected
at its very core”, while “there is nothing to prevent
genetically different cells in a clonal population from
also exhibiting stochastic behaviour”.

The upshot of his analysis is that while single-
cell analysis has shaken the GDP at its foundations,
the mainstream paradigm is still... mainstream. This
raises the question of how experimental results (in
this case, obtained by single-cell analysis) can actually
falsify a paradigm, and open the way to innovative
research directions.

Angleraux's article (which will be published in the
following issue, because of editorial reasons) follows
the same line of Heams, Racine and Paldi regarding
the need to clarify the theoretical ground of single-cell
analysis. She questions the type of biological explanation
underlying single-cell sequencing, and she applies general
frameworks in philosophy of biology (especially new
mechanism and systems biology) to specify how these
techniques explain biological phenomena. She comes to
the conclusion of a gap between the scientific narrative and what single-cell sequencing de facto produces.

Indeed, by combining different databases, single-cell analysis (as a kind of -omics sequencing techniques) aims to achieve a comprehensive and a more integrative explanation of biological phenomena, which matches with the *zeitgeist* against reductionism of current theoretical and philosophical perspectives in biology. New mechanism, on the one hand, claims to be non-reductionist because it takes into account emergent properties of organisms and explains them by integrating elements at different levels of description. Systems biology, on the other hand, also alleges a holistic view of life by combining biological subsystems. Angleraux examines to what extent single-analysis embraces new mechanism's and systems biology's *zeitgeist*. However, this is the case also because single-cell analysis shares the same theoretical and philosophical limits with these perspectives. In particular, both mechanism and system biology keep favoring bottom-up, rather than top-down explanations of living phenomena. As a consequence, Angleraux underscores the hiatus between the scientific narrative—what single-cell analysis declares to accomplish—and what it actually does for now.

We would like to thank the “Who am I? Single Cell Initiative” (WISCI), led by Pierre-Antoine Defossez at Paris Cité, and funded by the Labex “Who am I?”, which provided financial support for the workshop “Enjeux épistémologiques de l’approche «single cell»” held in Paris in May 31-June 1, 2022. The contributions to this special issue were initially presented at the workshop. We thank in particular Pierre-Antoine Defossez and Nikos Konstantinides who also attended the workshop and hosted Caroline Angleraux in their team as a postdoc. Their work provided a relevant case study to work on and think about single-cell analysis. Our warmest thanks go to the editors of the journal Organisms for accepting to publish this special issue, and to the reviewers of the articles for their rigorous and constructive critical comments.

## References

Ku, C, & Sebé-Pedrós, A 2019, “Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes”, *Philosophical Transactions of the Royal Society B: Biological Sciences*, vol. 374, art. 20190098.

Leonelli, S. 2019, “The challenges of big data biology”, *eLife*, vol. 8, art. e47381.

Naz, A, Obaid, A, Ikram, A, Awan, FM, Nisar, M, Paracha, RZ, & Ali, A 2019, “Systems-level understanding of single-cell omics”, *Single-Cell Omics*, vol. 1, pp. 433-456.

Qian, Z, & Bao, L 2019, “Cellular heterogeneity and single-cell omics” in D Barh & V. Azevedo (eds.), *Single-cell omics*, vol. 1, pp. 35-44, Academic Press.

Trapnell, C, Cacchiarelli, D, Grimsby, J, Pokharel, P, Li, S, Morse, M, Lennon, NJ, Livak, KJ, Mikkelsen, TS, & Rinn, JL 2014, “The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells”, *Nature Biotechnology*, vol. 32, pp. 381-386.

Wang, J, & Sang, Y 2017, “Single cell sequencing: A distinct new field”, *Clinical and Translational Medicine*, vol. 6, no. 1, p. 10.