Special Issue, Single-cell Analysis: Epistemological Inquiries

Vol. 6, No. 2 (2023)

ISSN: 2532-5876

Open access journal licensed under CC-BY

DOI: 10.13133/2532-5876/18306

# The Contrasting Role of Single-cell Studies in the Theoretical Debate on Determinism in Molecular Biology

Thomas Heams<sup>ab*</sup>

*Corresponding author: Thomas Heams, Email: thomas.heams@agroparistech.fr

## Abstract

In experimental biology, the last three decades have seen a flood of techniques dedicated to study biological phenomena at the single-cell level, and this article aims to reflect on how these technical advances can contribute to the renewal of theoretical perspectives in biology. The case studied here is that of the critique of the genetic determinism of molecular biology. The demonstration of unpredictability in gene expression at the single-cell level, a phenomenon known as stochastic gene expression, even in clonal populations, initially appeared to be a decisive indication that cells do not actually behave as predicted by deterministic frameworks. However, single-cell techniques have also revealed other sources of genetic variation that nuance this picture. The role of single-cell studies thus appears contrasted, and can be used to support or challenge the paradigm of genetic determinism (GDP). This opens up a more general debate on the practical ability of molecular biologists to criticize their own paradigms.

**Keywords:** single-cell studies, molecular biology, genetic variation, genetic determinism paradigm.

**Citation:** Heams, T 2023, "The contrasting role of single-cell studies in the theoretical debate on determinism in molecular biology", *Organisms: Journal of Biological Sciences*, vol. 6, no. 2, pp. 31-42. DOI: 10.13133/2532-5876/18306

## Introduction

The aim of this article is to observe and analyze the convergence of two different phenomena in biology: on the one hand, a long-running debate on the relevancy of determinism in molecular biology, and, on the other hand, the rise of single-cell studies, based on techniques allowing to analyze cells not just at the population level, but actually one by one. Although these techniques are often adaptations at the cellular level of previously mastered molecular biology techniques, they have not spontaneously appeared as neutral and inevitable technological improvements of the former ones:

they have also, and probably mainly, responded to a growing theoretical interest for the single-cell scale in organisms. The desire to know each cell more and more precisely in its context, and the intuition that certain biological questions would need to achieve such a level of precision, are fundamental and not merely technical questions. They also have played a role in—and thus form the first link between—theory and practice in molecular biology. Further, these techniques appeared crucial for criticizing the theoretical soundness of a key dimension of molecular biology, namely its deterministic foundations. This article aims to explore the extent to which single-cell studies have been

<footnote><sup>a</sup> AgroParisTech, département SVS, Palaiseau, France</footnote>

<footnote><sup>b</sup> INRAE, UMR Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France</footnote>
mobilized to produce this critique, and the extent to
which they have also shown their limitations in doing
so. This will in turn raise questions about the nature of
plastic thinking in science, as in the case of the currently
dominant paradigm in molecular and cellular biology.
Is its ability to resist and even metabolize the various
criticisms a sign of its enduring relevance? Or is it
rather a worrying symptom of a way of doing science
that prefers to construct narratives and feed them,
rather than consistently keeping self-critical and aware
of its own aporias?

# 1. Thirty Years of Single-cell Approaches in the Era of Genetic Determinism

It is worth clarifying at the outset what this article
means by single-cell techniques. These techniques
have been developed for some thirty years. Of course,
observations at the single-cell level have been made for
a long time: microscopic observations and histological
sections, among many other approaches, are sometimes
as old as biology. Single-cell techniques are in fact a set
of molecular and cellular biology techniques originally
designed for use on populations of molecules or cells.
Their advancement in precision and efficiency allowed
use on individual cells. This is the case, for example,
of PCR, which was developed in the early 1980s, and
which, thanks to technical improvements, has been
used on isolated cells in the following decade, as
reviewed by Kehr (2003). In this context, single-cell
approaches are understood as those that enable the
identification of content and expression of genomes on
a single-cell scale. In concrete terms, these are mainly
genetic material amplification techniques such as PCR
(for DNA) and RT-PCR (for RNA), and the various
-omics approaches: genomics (Gawad et al. 2016),
transcriptomics (Longo & Hasty 2006; Kolodziejczyk et
al. 2015), proteomics, metabolomics and epigenomics
(Bheda & Shneider 2014), and all these combined
(Wang & Bodovitz 2010). Single cell techniques can also
rely on fluorescent markers on living cells (Elowitz et
al. 2002) and/or take advantage of recent development
in flow cytometry, a decades-old technique able to sort
cells one by one, now upgraded with new analysis
markers and methods (Di Carlo & Lee 2006), and of
the use of microfluidics (Templer & Ces 2006).

Presented this way, we can already see that single-
cell techniques are tools that molecular biology research

has used to reinvest in a long-neglected scale. To better
understand such negligence, it appears necessary to
explicit some important epistemological driving forces
that are at stake in this discipline. Indeed, molecular
biology, as a discipline, is based on an instructionist
and deterministic paradigm, which is also the starting
point of its research program. It can be stated as
follows: in multicellular organisms, cells are seen as
sending and responding to intracellular, intercellular
or environmental *instructions*, *determined* through
precisely regulated molecular reactions, and the
functioning of the multicellular organism relies on
intense intercellular coordination through the proper
integration of these signals. Obviously, molecular
biology first focuses on *molecules*, but with the goal
of integrating these molecular interactions into a
broader picture at the cellular and multicellular level,
for which this paradigm is the consensus framework.
Here, the somatic cells of the multicellular organism
are assumed to be genetically identical, and the
evolutionary rationale behind this coordinated
functioning is that it is a profitable strategy, in
Dawkins' terms, for maximizing the diffusion of each
cell's genes via those that will be transmitted by the
gametes emitted by the organism.

That this starting point should be considered as
a paradigm, as will be the case in the remainder of
this article, may seem obvious to some. However,
others may see it as a strong stance that needs to be
justified—even more so as it also implies underlining
what we mean by molecular biology. Indeed,
molecular biology has at least two facets: (1) on the
surface, it is a practical, experimental discipline,
characterized by the level at which it proposes answers
to biological questions—that of biomolecules. In this
sense, molecular biology has a pragmatic dimension
that may seem at odds with the existence of rigidly
fixed paradigms, especially if they are not explicit.
This facet of molecular biology undeniably exists, and
in fact, most molecular biologists do not engage in
theoretical debates on the fundamental motivations of
their discipline, which are rather restricted to a small
number of research groups. Such is the case with the
debate that will be the subject of this article, whose
audience is as limited as its importance is crucial.
Seen under this light, the very idea of molecular
biology existing under the imperium of a paradigm
may seem critical, even misleading.
In fact, molecular biology is not just that. It is
also (2) a DNA- (and RNA-) centric vision of biology
with a specific history, and its own sequencing and
modifications. It postulates *de facto* that DNA is the
organizing principle of living organisms, as evidenced
by its lexicon imported from computer science: genetic
code, genetic program, etc. It is therefore intrinsically
deterministic (Noble 2006). This vision of biology,
which has produced countless experimental results,
is structured around a strong assumption: the search
for precise explanations in biology is supposed to find
its answers in the precise functioning of organisms.
However, this assumption articulates two very different
ideas: while it is logical that a discipline should seek
precise answers to explain phenomena, there is nothing
to imply that the objects studied provide these answers
insofar as they themselves are necessarily precise. The
indissoluble link between these two levels of precision
indicates that molecular biology relies on strong but
implicit theoretical presuppositions, the foundation of
which is the aforementioned starting point. Molecular
biology is also the dominant vision of biology at
present, so we feel that it deserves to be called the
Genetic Determinism Paradigm (GDP), even if its
determinism is probably less rigid than those of other
scientific disciplines. The fact that many practitioners
do not necessarily experience it as deterministic is
not in itself a contradiction or a counter-argument. In
the Kuhnian sense of the term, the establishment of a
discipline paradigm is followed by a phase of normal
science that the majority of scientists exploit and take
for granted without questioning or even imagining that
it could be questioned, until the paradigm finally enters
into crisis.

Acknowledging this deterministic framework clarifies
why, in this context, the observation of individual and/
or single cells has long been regarded as anecdotal:
apart from the technical challenge involved, all cells of a
given organism were assumed to be genetically identical
and all cells of a given tissue were assumed to behave
identically on first approximation. Indeed GDP was
based on a postulate of homogeneity: all cells in the same
tissue, receiving the same signals, react in a similar way
because they possess the same genes. Hence, in order to
find out how much RNA or protein is produced in a cell,
this theoretical framework measures such a quantity in a
large sample of cells and deduces the individual quantity
by a simple division. In so doing, GDP largely overlooks

any consideration of intercellular variability in gene
expression other than residual.

Nevertheless, single-cell observations made sense
in certain areas of experimental biology, notably in the
study of the early stages of embryonic development,
of precisely located small groups of neurons, which by
definition involve minimal cell numbers. The 1990s saw
the first significant wave of publications on single-cell
approaches, notably by single-cell PCR (amplification
of DNA enabling an approach to the genetic content)
and then by RT-PCR (amplification of RNA, enabling
an approach to the genetic content use) (Kumazaki
et al. 1994). In the context of molecular biology's
general research program, these techniques were
primarily designed to increase precision at a scale
that had long been inaccessible. While the majority
of these articles were mostly technical, some others,
using complementary techniques as fluorescent in situ
hybridization, soon started to challenge experimentally
the deterministic nature of gene regulation (Wijgerde
et al. 1995).

Over the past thirty years, increasingly
comprehensive atlases and databases documenting
hundreds of cell types have been made available,
providing access not only to genomes, but also to
the transcriptomes, proteomes and epigenomes of
particular cells, representing their cell type (Elmentaite
et al. 2022). This is the continuation of one of the great
projects of molecular biology, which is the mapping
of living organisms on all scales, from the genomes
of species, to the transcriptomes of cell types, to the
microbiota of various environments. Concentrated on
the so-called informational molecules (DNA and RNA),
this trend towards producing collections, which is as
old as biology itself, has found a new lease of life in
the era of Big Data, where the single-cell scale appears
as an additional dimension in the completeness of
this undertaking. In this context, single-cell studies
have continuously addressed an ever-wider range of
biological issues, either fundamental or applied, from
microbiology and plant sciences to medicine (where an
intense focus of research is devoted to cancer research
and tumor heterogeneity [Liang & Fu 2017]).

## 2. Challenging GDP

These techniques appeared in a period of GDP's
triumph and expansion, which could pragmatically be
called the era of the genetic program (for and in-depth
critical analysis, see [Noble 2006]). The very existence
of a genetic program (etymologically: “written in
advance”) encoded in the genome of organisms is the
dominant idea at the time and the GDP core, with strong
or weak nuances to this largely consensual principle.
The more nuanced versions admit that this program
is either more flexible and can be reprogrammed (see
the research on stem cells), or that it is not strictly
genetic (see the research on epigenetics), or that it is
open to external influences (see the criticism of the
“all-genetic” approach). However, the fact remains
that, at the time, and most probably even today, it was
difficult to propose approaches that would radically
dispense with the notion of any genetic program. In
fact, single-cell approaches soon produced results that
cracked the epistemological edifice of GDP. Further,
in the same period, Elowitz and colleagues published
a paper that, right from its title, caused a stir in the
scientific community working on gene expression
(Elowitz et al. 2002). Thanks to fluorescent markers on
live bacterial cells, they showed that the default genetic
expression state of a clonal bacterial population, in a
controlled homogenous environment, was diverse and
unpredictable. Mainly thanks to this paper, the issue
of stochastic gene expression, postulated more than
twenty years before (Spudich & Koshland 1976; Kupiec
1981, 1983), made a dramatic entrance into the scientific
debate, as if it had been discovered on this occasion.
Single-cell approaches showed decisive (McAdams &
Arkin 1999; Raj & van Oudenaarden 2008) to unravel
this long-hidden dimension of gene expression, that
hand long been obscured by average values when
measured at the cell population level, based on the
postulate of homogeneity.

Thinking began to unfold around this counter-
intuitive phenomenon in GDP, since cells no longer
seemed to respond to each other in a coordinated
fashion. On the contrary, they randomly explored
avenues of behavioral adaptation to their immediate
environment. Thanks to single-cell approaches,
mechanistic causes were highlighted, such as
macromolecular crowding (Ellis 2001), which
creates topological differences between cells. The
postulate of availability has also been challenged:
the overwhelming majority of proteins are found to
be present in less than a hundred copies per cell on
average (Guptasarama 1995). For these, the law of

large numbers does not apply, and this intercellular
variability creates sampling and threshold effects,
even within functionally homogeneous and genetically
identical cells of the same tissue (this will be
questioned below). Topological competition exists for
certain supposedly regulatory molecules: they cannot
be present on all their potential molecular targets,
which also generates variability. In this context, it has
also been documented that maintaining the biological
order, regularity and precision of genetic regulations
requires a correlated expenditure of energy (Lestas et
al. 2010). The evolutionary rationality of GDP, which
relies on these regulations, thus turned out to depend
on the cost/benefit ratio of maintaining this order, and
in so doing, partially lost its self-evident character.
At this point, the aforementioned progresses in
molecular biology allowed to start making sense of
stochastic gene expression, both mechanistically
and statistically. That being said, considering that
“nothing makes sense in biology except in the light
of evolution” (Dobzhansky 1964) a crucial question
remained: if it can be explained mechanistically, what
is the evolutionary rationality of this unpredictable
variability of gene expression? In other words, what is
its biological logic (Pearson 2008)?

3. Making Sense of Stochastic Gene
Expression: From Damage Control to
Radical Rethinking

The awareness about a non-programmed dimension
of gene expression leads to, broadly speaking, three
main classes of hypothesis.

(i) The first concedes the existence of this
intercellular variability of expression, but relegates it to
a status of parasitic background noise. This hypothesis
is compatible with defending GDP, confining this
variability to the status of a margin of uncertainty. This
point of view has many supporters, notably in synthetic
biology. This young multidisciplinary approach is often
presented as the cutting edge of experimental biology.
It aims to reconfigure living organisms radically for the
purposes of both fundamental knowledge and varied
applications: if stochastic variability in gene expression
is widely studied here, it is with the main concern of
taming it, of reducing it to enable small cellular chassis
to function reliably and reproducibly. Under the guise of
being disruptive, synthetic biology is above all the new
garb of GDP taken in its most literal sense, where living entities, whether cellular or multicellular, are explicitly compared to fine-tuned machines, and in which chance can be little more than a disturbance.

(ii) The second hypothesis is that the unpredictable intercellular variability of gene expression is a form of valve, an opportunity to relax the genetic program considered too schematic, yet without disqualifying it. This is where classical determinism is described as being able, in local situations, to accommodate or even make use of random gene expression. Biological literature describes numerous cases of bistable equilibria in genetic networks, which can produce diversity. One example is the different versions of rhodopsin (Wernet 2006), a wavelength-sensitive pigment, in the compound eyes of Drosophila, whose units are small groups of cells called ommatidia. These organs have two types of rhodopsin that enable them to capture two complementary wavelength spectra, and the relative proportion of ommatidia producing one or the other is a biological parameter of adaptation to a given environment, and therefore subject to natural selection. This is not a highly complex regulatory system controlling the proportions of expression of each version of the rhodopsin molecules in the ommatidium concerned. In fact, it is a simple molecular regulation— the bistable equilibrium—favoring the expression of one at the expense of the other in each of the ommatidium, with a differential affinity that may be the product of natural selection. On the scale of the individual cell/ ommatidium, we cannot predict which rhodopsin will be synthesized. We can measure probability, but on the scale of the cell population composed of all ommatidia, this probability becomes a proportion which is, in turn, predictable. The proportion adapted to the environment is thus achieved without instructional coordination between cells. As we can see, this approach stems from a desire to reconcile GDP with the flagrant manifestations of phenomena that challenge it. The solution adopted is in fact to consider that these phenomena are a complex genetic trait (Ansel 2008), i.e. a trait itself driven by a certain number of loci in a given genome, just as an individual's height or weight might be. In this reading, stochastic gene expression is no longer opposed to GDP, but it becomes one of its possible outcome.

(iii) The third hypothesis brings in a more radical challenge. It consists in opposing GDP with an alternative framework, actually based on stochastic

gene expression. We are going to call this framework as the probabilistic alternative framework (PAF). Here, stochastic gene expression is a fundamental biological parameter, as opposed to its status as a margin or valve in the previous classes of hypotheses. PAF explains what we take to be coordinated responses in GDP in terms of the exploratory behavior of cells. This means that, in a given context, cell exploit differentially, and largely blindly, a genome that is nonetheless common (hence the stochastic expression of genes). The genome is no longer an instruction manual as depicted by GDP, but rather a reservoir of possibilities. Response accuracy is not achieved by the docile obedience of cells to a rigid program, but by the fact that some of these cells find adapted solutions in this probabilistic exploration of their genome's possibilities, and are thus selectively favored in their local environment, in a sort of Darwinian process based on gene expression differences rather than actual genetic differences.

PAF relies on cell selection, which may seem odd in the context of GDP but has indeed a long history. Its beginnings can be found in Denis Diderot's *D'Alembert's Dream* (1769) in which the famous thinker portrays the physician and philosopher Théophile de Bordeu, to whom he lends the idea that each organ in the organism has its own will, and hence its own particular interests. A century later, competition between parts is at the heart of the seminal work of the founder of German experimental embryology, biologist Wilhelm Roux, *The Struggle of the Parts in the Organism* (Roux, 1881, Heams, 2012). Roux, after a Darwinian reading not free of ambiguities, explores the hypothesis that cellular subsets, cells, tissues and organs are characterized by natural selection dynamics at their respective scales. The revolutionary Darwinian hypothesis of the creation of a biological order based not on a superior will, but on the dynamics of chance and selection, is here transposed to the interior of organisms. In the course of the twentieth century, several theories concerning certain major physiological functions incorporated a selective component that is no longer debated, such as clonal selection for immunity, or the selective stabilization of synapses for brain development. These and others were analysed in a 1993 review by James Michaelson, outlining a landscape in which selective dynamics at least questioned the primacy of GDP. As mentioned, biologist Jean-Jacques Kupiec first laid the theoretical foundations of PAF in the early 1980s
(Kupiec 1981, 1983). He would refine his study for over thirty years, based on the dynamics of chance and selection as a modality of cell differentiation, embryonic development, and regulation. It is in the light of these all-too-brief historical reminders that we should appreciate the return of the theme of cell competition in the 2010s (de Beco et al. 2012) as revisited by new single-cell techniques. The portfolio of techniques now available has provided new life to the theme of cell competition, which is based precisely on differences between cells. This, until a resounding publication made it to Nature's cover with the headline "Battle Lines—Life-and-death Competition Between Cells in the Mouse Embryo" (Volume 500 Issue 7460, 1 August 2013). No doubt this is a sign that, at the very least, the journal considered this theory as innovative.

In addition to having a strong genealogy, which after all is not in itself a proof of validity, PAF also has some undeniable epistemological complementary forces of different natures. First (i) it gives a primary biological meaning to the phenomenon of stochastic gene expression, which pervasiveness has been confirmed decade after decade in particular thanks to single-cell techniques observations. Secondly, (ii) it is economical in terms of hypothesis, since it obviates the systematic need of devising biological explanations for complex, energy-intensive regulations. What is more, (iii) it is all-encompassing, since it can accommodate apparently deterministic phenomena (i.e. highly reproducible, systematically observed or almost so) by considering them as probabilistic with a probability of occurring close to 1. Furthermore, (iv) it is supported by general observations that GDP instructionist lenses cannot explain but poorly, such as the significant cell death observed during cell differentiation, or the transient increases in gene expression variability that precede cell differentiation phases (Buganim et al. 2012; Dussiau et al. 2022, Parmentier et al. 2022) predicted by proponents of this new theoretical framework (Heams 2004). Finally, (v) it proposes a unifying perspective of biological phenomena, since it is based on Darwinian-type dynamics of chance and selection: by proposing to import them into multicellular organisms, it relativizes the need to base these dynamics on sometimes murky and arbitrary additional principles of higher organization, such as the predicate that the organism is at the service of its genome.

4. The Probabilistic Alternative
Framework (PAF) and its Critics

As with any theoretical proposition targeting the core of a dominant and productive discipline, PAF had to prepare to face substantial criticism. In this situation, its proponents, actively engaged in proposing a new framework through experimental demonstrations, were not in a symmetrical situation with the vast majority of biologists who were taking GDP for granted and, rather than explicitly defending it, were mostly validating it by default. At its very roots, this was an imbalanced situation. Further, GDP had already faced waves of criticism, due to other theoretical frameworks, such as organicism, or due to new trends in experimental research, such as the aforementioned epigenetics research that, among others, challenged a gene-centric view of biology.

One strong criticism is that PAF takes the stochastic aspect of genetic expression for granted, without being able to prove it, and it cannot deny that an underlying order may be lying behind this apparent disorder. This objection is not, however, likely to shake its foundations (Heams 2014). First of all (and leaving aside the general fact that asking for a proof of non-existence is not generally considered a valid scientific critic), the question of the existence of true randomness is metaphysical. Like so many others before him, Charles Darwin himself insisted on how cautious one has to be when dealing with the “chance”, and when himself was doing so, he strongly stressed he did not exclude that the “chance” could be linked to the ignorance of certain causes (Darwin 1859, introduction of Chapter 5). But this did not detract from his main point: what is important in the mechanism of chance and selection, called natural selection, is not so much that the chance is “true”, but that the variations due to this chance are independent, uncorrelated with the following selection. This also applies to PAF within the multicellular organism itself. PAF does not aim to discuss whether the unpredictable variability of stochastic gene expression is an ontological disorder or an appearance of disorder based on a subjacent order yet to be discovered, but to challenge the very logic of GDP, where, at the very scale at which it is described, genetic regulation is presented as precisely ordered. In this context, the hundreds of scientific articles providing experimental evidences of stochastic gene expression have delivered a clear
verdict: stochastic gene expression is observed across
the board (Sood & Misteli 2022). While this does not
prove that PAF is a more convincing framework than
GDP, it does push GDP and its logic based on fine-tuned
regulation of gene expression to their limits.

Another objection to PAF logic, rarely stated explicitly, is in fact so decisive that it deserves careful consideration. It consists in pointing out that any framework based on the stochastic behavior of individual cells goes against one of the most important presuppositions of evolutionary biology. This is important because PAF is drawing inspiration from Darwinian dynamics. The context of this objection is the following: in a conceptual framework where it is generally accepted that unicellular organisms compete for resources, one of the key questions of biology is to explain how a cooperative behavior can emerge in a cellular collective that we call a multicellular individual. Yet—and this is essential to understanding the extent to which the program paradigm is a pillar of both molecular and evolutionary biology—the consensus in evolutionary biology is that the emergence of multicellularity required the repression of the collective cells’ “selfish” (i.e. uncoordinated for the benefit of the organism) behaviors. This leads to the strong implicit objection that the cells of a multicellular organism cannot constitutively behave in a stochastic, thus selfish manner. In the words of evolutionary biologist Richard E. Michod:

> "For multicellular organisms to emerge as a new unit of selection, the selfish tendencies of their component cells had to be controlled. Theoretical results indicate organisms may regulate this internal conflict and competition (...) by directly reducing the benefits to cells of defecting" (Michod 1996).

This is the keystone of the evolutionary explanation: the evolutionary trend towards multicellularity must be accompanied by a reduction in the unpredictable behavioral variability of individual cells, in favor of coordinated collective behavior. In other words, if cell behavioral stochasticity exists, then it can only be residual, probably belonging to an ancestral subsistence (Lehner 2008).

As crucial as this initial presupposition may be, it is not immune to criticism. Here, we will focus on one major objection. This stems from the very structure of the theoretical demonstration that leads to the

decree that the rise of cellular cooperation is necessary for the emergence of multicellular individuality. Its formal model compares two categories of theoretical cellular individuals that differ in particular at a given locus, being either a defector or a cooperator. This model shows that, in this framework, defective non-cooperative individuals are at a disadvantage. But this fictitious situation has nothing to do with stochastic gene expression. The defective/cooperative locus hypothesis is based entirely on the deterministic functioning of the gene in question, where this locus appears as a switch that causes a bifurcation in the genetic circuit: it is a GDP-based hypothesis that cannot therefore, by construction, be used to decide between a GDP and a stochastic (and even any) alternative. Even supposing that the modelling would have led to the opposite result (advantage of the defective allele over the cooperative allele) it would still have been useless in deciding between GDP and PAF. In short, the two approaches are incommensurable. It follows that the consensus about the development of the multicellular state requiring the emergence of intercellular cooperation—whether one agrees with it or not—only makes sense within GDP, and cannot be used as an argument to weight the merits of this paradigm against others, nor a fortiori to disqualify the latter. Moreover, its influence is not absolute. The idea that cells must cooperate entirely within the higher unit that is the organism is open to debate. Evolutionary biologist Leo Buss proposed to explain multicellularity not as the eradication of all non-cooperative behaviors, but as a more subtle balance between different tendencies. He points out, for example, that

> "(cell) variants that favour both the proliferation of the cell lineage and the organism harbouring them were sequentially incorporated in an increasingly sophisticated epigenetic program. In contrast, variants that favour the replication of the cell lineage at the expense of the individual were eliminated and ultimately favoured the fixation of variants that limited the production and/or expression of subsequent variation, creating a stable developmental system" (Buss 1987).

It is therefore not inevitable that cells should have only one possible (cooperative) behavior; they can have a more unpredictable and less coordinated component, provided that this component is not such as to compromise, in return, the proliferation of the organism containing them. This suggests that it is
possible to envisage a range of ways of producing an
appearance of cooperation or coordination, a spectrum
of possibilities, and not just one modality that would
be the strict unconditional cooperation of every cell, at
every moment, for the benefit of the organism.

Here, it seems relevant to note that these two
main objections to PAF do not stem from molecular
biology itself and its tools meant to refute or reinforce
a hypothesis: the former concerns the metaphysics of
chance, the latter a key aspect of the evolutionary theory.
This situation is not due to the impossibility of subjecting
PAF to experiment: as mentioned above, not only the
evidence for generalized stochastic gene expression
keeps growing stronger, but also theoretical predictions
specific to PAF and hardly compatible with GDP, such
as the transient variation in the intensity of stochastic
gene expression during cell differentiation, have been
documented. Still, GDP remains dominant: the question
facing molecular biology, then, is the level at which
criticism of its conceptual underpinnings must take place
so that a critical examination of these underpinnings can
be undertaken in a demanding manner.

5. When Genetic Variation in Clonal
Community Comes into Play

The back-and-forth between theory and
experimentation was further complicated by another
turn of events. This is the realization that genetic
variability exists within clonal cell populations, in
particular somatic cells derived from the fertilized egg
of a multicellular organism (O'Huallachain et al. 2012;
Ogawa et al. 2022). Contrary to what was long been
thought, to the extent that the term "clonal" has become
synonymous with "genetically identical", the cells of a
multicellular organism, although clonal in the sense that
they originate from the same egg cell, can in fact exhibit
considerable genetic variation between themselves. As
we shall see, this will challenge the relevance of the two
frameworks, but for different reasons.

Both frameworks implicitly assume that two cells in
the same organism are genetically identical, and that all
other things being equal, it is on the basis of this similar
gene endowment that we must explain the emergence
of difference, namely cell types and their apparently
coordinated functioning. GDP states that biological
order is achieved by coordinating cells via their response
to intercellular or environmental signals, while PAF
proposes that this order is at least partly achieved by

a dynamic of chance and selection. The reliability of
the former is based on the precision of regulations,
while the reliability of the latter is based on statistical
reproducibility derived from the principle of the law
of large (cell) numbers and the recurrence of certain
micro-environmental constraints at certain stages of
embryonic development and cell differentiation.

Against this backdrop of competing explanations,
awareness of the significant mutability of somatic cells,
while not new, is becoming, for both frameworks, an
issue at the heart of this debate as biologists become
increasingly aware of its magnitude. The inescapable
potential for cell mutation has long been considered
to derive from the residual error rate in the precision
of genetic duplication during mitosis. The enzymatic
apparatus controlling and correcting the appearance of
"errors" in the copying of the new DNA strand, while
remarkably reliable (and everything suggests that
this is a parameter of natural selection), nevertheless
admits a residual error rate whose order of magnitude
is maximum one mutation per cell division in a
genome. These exceptions were often analyzed as a
source of possible explanations for the appearance of
cancerous dynamics within an organism, where a cell
would mutate and adopt a selfish behavior contrary to
the default coordination existing between cells sharing
the same genome. Advances in molecular and cellular
biology have overturned this order. First, there has been
a growing awareness of the multiplicity of sources of
genetic differentiation between clonal cells. In addition
to the residual mutation rate described below, a series of
phenomena have been added that, although disparate,
all contribute to the creation of genetic variation between
clonal cells. These include transposable elements,
variations in copy number, traces of viral infections and
horizontal exchanges (Ogawa et al. 2022). The immune
system, for example, produces lymphocytes that are
all genetically different at certain loci, in line with the
broadest possible capacity to detect the widest possible
range of antigens and activate the immune response.
This generation of diversity obviously reaches its peak in
the context of gametogenesis, which produces haploid
cells that are all genetically different from one another.

Secondly, the vision of an eukaryotic genome
composed of a few functional sequences drowned in an
ocean of “neutral” or “useless” sequences (according
to the old dichotomy of coding DNA versus non-
coding DNA) has been shattered by at least two major
discoveries. These are: (1) the genome’s significant
expression activity well beyond the three major classical
RNA families (mRNA, rRNA and tRNA), with entire
sections of the genome long considered to be non-
coding now known to be in fact active; and (2) the many
potential RNAs and proteins that can often be produced
from a single DNA sequence through alternative splicing
and editing. Each of these phenomena come with their
own complex regulation "rules". All this contributes to
postulate such a complex cross-regulatory dynamic that
it is a challenge to intelligibility, a fortiori when viewed
from the GDP perspective.

Taken together, these two major phenomena imply
that the hypothesis of the broad genetic homogeneity
of clonal cells must be seriously relativized. This, in
turn, raises formidable questions for the theoretical
frameworks used to explain how organisms function.

## 6. Challenged, but Not in the Same Way

On the one hand, GDP can be seen as temporarily
strengthened by this realization. Indeed, one of its major
epistemological aporias is to presuppose difference
in order to explain the appearance of difference. For
example, when it is claimed that cells receive signals
from other cells, which induce them into their own
specialization or differentiation, this is based on the
presupposition that there is a pre-existing asymmetry
between the sending and the receiving cells. Of course,
prior differentiation can explain this asymmetry ad
hoc, but then the explanation is displaced without
really being answered and, even more problematic,
the existence of a difference between cells becomes
both the *explanans* and the *explanandum*, creating a
strong risk of *reductio ad infinitum*. In this context,
awareness of the multiplicity of spontaneous sources
of genetic differences between clonal cells within an
organism can be seen as a providential windfall that
relieves GDP of the responsibility of resolving its
initial contradiction. Indeed, this is a Pyrrhic victory,
for these phenomena in turn have far more severe
consequences for its underlying logic. Clearly, they
sweep away the idea that the coordinated collectivity
that is supposed to be the sum total of somatic cells
can be so as a strict consequence of Dawkinsian
selfish gene dynamics. Strictly speaking, somatic cells
can no longer be described as working together to
maximize the organism's longevity and the probability
of gamete transmission of copies of their shared gene

pool, since this pool turns out to be heterogeneous.
To put it another way, GDP initially rests on the idea
that organisms function in a coordinated and precise
manner because they are genetically homogeneous,
and indeed measure the consequences of this when
genetically different rogue sub-units appear (e.g.
tumors). But this fundamental genetic homogeneity
is increasingly being undermined. GDP is therefore
unable to explain the rationality of the coordinated
functioning of cells that are genetically heterogeneous,
yet explaining this coordinated functioning is nothing
less than its *raison d'être*.

Also the realization of the unsuspected extent of
genetic heterogeneity within a clonal cell population
of an eukaryotic organism challenges PAF, but it
should be noted that this challenge is of a different
order. The probabilistic explanation is largely based
on the assumption that, *all other things being equal*
at the cellular level, unpredictable cell behavior
is observable. There is no need to presuppose any
genetic differences between cells to explain their
different behaviors. On the contrary, it is only on
the basis of exploratory or even stochastic dynamics
that cells can differentially use the same genomes to
produce behavioral differences (i.e. differences in the
way this common genome is exploited, mainly but
not exclusively through the mechanism of stochastic
gene expression). In concrete terms, an experimental
demonstration of this functional power of stochastic
expression potential is based on population
observations of genetically identical cells placed in the
most homogenizing conditions possible (same micro-
environment, same cell cycle state). Cells are observed
one by one as far as possible with adapted single-cell
techniques, so that any observed behavioral variability
(e.g. in transcription, translation, methylation)
can then be attributed to a stochastic rather than
programmed behavior, since these cells have the
same gene content but behave differently. This clearly
illustrates the challenge that genetic heterogeneity in
clonal cell populations represents for this framework:
the greater the heterogeneity, the more difficult it is to
maintain the starting hypothesis of this experimental
demonstration. Genetic heterogeneity within clonal
populations acts here as a hidden variable, providing
a possible "classical" explanation for differential
behavior: it would not be based on random behavior
with a constant genome but, much more classically, on
a long unsuspected difference in genetic composition,
giving a potential selective advantage to some.

Both frameworks are therefore put to the test by the
discovery of genetic heterogeneity in a clonal population,
or at least by the extent of it. However, it should also
be noted that the two frameworks are not affected in
the same way. As wed have seen, GDP is affected at its
very core. Designed to explain how variety can appear
in the functioning of cells that are genetically identical
and thus linked by a Dawkinsian-type community
of destiny, GDP is criticized in its very starting
presupposition: these cells do in fact differ genetically.
According to consensus evolutionary principles, there
is nothing to prevent them from exhibiting selfish or
defective behavior, but this is not the case (apart from
pathological situations). GDP may therefore be able
to continue to explain the ways in which intercellular
coordination is acquired, but it is unable to explain the
evolutionary rationality of this maintenance, unless one
enters into circular reasoning.

Likewise, PAF is challenged by the unsuspected
extent of intercellular genetic variability in clonal
populations, but this is a different kind of difficulty.
Now, demonstrating that gene expression is stochastic,
for example, requires an additional precaution, and
perhaps a serious experimental headache. In fact, PAF
needs to prove something previously taken for granted,
i.e. that the clonal cells under study do not have an
unsuspected genetic variability that would explain their
differential behavior. This is a formidable experimental
challenge, but it does not have the same epistemological
status as the one faced by GDP. PAF is not weakened at
its core; it just needs to be more cautious than it allows
itself to be. Indeed, a probabilistic framework is based
on the stochastic behavior of genetically identical cells,
but there is nothing to prevent genetically different cells
in a clonal population from also exhibiting stochastic
behavior. In short, genetic variability here is rather
added to gene expression variability in the generational
sources of fate diversity between cells of the same
clonal origin. The risk PAF may run is that it may fail
to disentangle the causes (genetic or non-genetic) of
stochastic cellular behavior, but not to minimize it,
and genetic variability in clonal populations is not an
observation likely to refute the intrinsic or extrinsic
molecular causes of stochastic gene expression.

The fact that these recent approaches challenge both
the frameworks shows how versatile is the role of single-

cell studies in debates about genetic determinism. In
fact, they can challenge determinism to explain so much
stochasticity in supposedly precise and reproducible
regulations, as well as they can rescue it by discovering
countless unsuspected and providential sources of
genetic variations in homogenous cells. This casts doubt
on the possibility of using experimental approaches in
molecular biology to compare and assess the relative
validity of two competing theoretical models.

Conclusion

In this schematic opposition between two
frameworks, one might think that single-cell approaches
could have played the role of justice of the peace: the
more manifestations of stochastic gene expression
were found, the more GDP would be challenged.
Yet, after thirty years of development of single-cell
techniques, GDP is resisting. The dominant discourse
in molecular and cellular biology is admittedly more
nuanced than it was half a century ago, but it remains
deterministic at its core. This is stressed by the calls
for projects that, from genes to genomes, most often
continue to aim for their exhaustive description, with
a view to eventually producing ever more sophisticated
syntheses of all the cross-relationships between genes.
Not to mention, of course, the economic context in
which this research program is unfolding, where the
atomization of organisms into stocks of genes to which
a precise task can be assigned within a network of
precise regulations, is compatible with patentability
and therefore commercial appropriation. In addition,
one also must not underestimate the power of the
imaginary that emanates from GDP: it places biologists,
or whoever controls biological processes, in the
position of demiurges, able to modify living organisms
by bioengineering in the same way that engineering
can modify machines. It also contributes to create a
reassuring narrative of our biological condition that
leaves no room for the distressing dimension of chance
in our daily functioning, or in our origins. All of this
probably makes GDP much more than a dominant
scientific framework. Therefore, the balance of power is
not that of two theoretical frameworks of equal strength.

It follows that, with the benefit of a few years'
hindsight, the contribution of single-cell studies to the
clarification of theoretical biases is ambivalent. It is
undeniable that interest in the cellular level has made
biologists more sensitive to cellular individuality, and
to the delusional nature of the cellular homogeneity
postulate. But it can also be said that this profusion
of results, which one might have thought would allow
for deciding between different theoretical frameworks,
ultimately seems to alter the balance of forces very little.
GDP remains largely dominant, as if it had metabolized
the genetic heterogeneity of clonal populations and
certain explicitly probabilistic dynamics, as special cases
that do not call into question its founding principles.
From this perspective, the little machines that cells are
supposed to be are certainly less reliable than expected,
but remain little machines nonetheless: at the very
least, this does not do justice to the intense theoretical
debates within biology over the machine conception of
organisms with strong proponents (e.g. Bongard & Levin
2021) and opponents (e.g. Nicholson 2013). The issue
at stake here is not to regret or support this persistence
of GDP, but to engage in a collective discussion on how
to question its relevancy, that seems overshadowed by
its dominance. Such a paradigm can thrive because it is
scientifically fruitful and productive, but also because,
when dominant, it relies on its past successes (more
than its own updated merits) to raise the bar high for
any potential contestation. In other words, an important
inertia can exist even if it is convincingly criticized.
Further, this dominance comes with a powerful
narrative, the above mentioned possibility to “engineer
life” as we do for machines. Many researchers got
acquainted with it, get advantages from it (in particular,
a position of power), and thus hardly accept to let it
go, even when a substantial number of observations
challenge and even undermine it. Because of their
aforementioned versatility, single-cell techniques have
not been, so far, an efficient tool for this much needed
falsifiability, even though they still have potential in this
respect as well as in many areas of theoretical research
in biology. The landscape we have described here has
shown that the sources of genetic variability between
cells within an organism are multiple. Moreover, they
must be combined with even more radical sources of
variability: the somatic cells of a multicellular organism,
notably metazoans, cohabit with others, the cells of
our microbiota, or even the cells of maternal origin
that make each of us chimeric, mosaic individuals. In
short, different ways of being different contribute to
shaping individuals. Understanding the overall logic
and functionality of all these sources of variability is

a new frontier and a story yet to be told: do selective
and instructional dynamics cohabit? Do different
selective dynamics co-exist? Is there a competition of
competitions between these different sources of genetic
variability, and the cell populations that embody them?
These open questions are crucial, and it would be
desirable for single-cell approaches to tackle them head-
on, rather than feeding the endless quest for details that
molecular biology loves to accumulate.

## Acknowledgements

The author would like to thank the reviewers for their
comments, which have helped to make the manuscript
clearer and more coherent.

## References

Ansel, J, Bottin, H, Rodriguez-Beltran, C, Damon, C, Nagarajan, M, Fehrmann, S, et al. 2008 “Cell-to-cell stochastic variation in gene expression is a complex genetic trait”, *PLoS Genetics*, vol. 4, no. 4, art. e1000049.
https://doi.org/10.1371/journal.pgen.1000049

Bheda, P, & Schneider, R 2014 “Epigenetics reloaded: The single-cell revolution”, *Trends in Cell Biology*, vol. 24, no. 11, pp. 712–723.

Bongard, J, & Levin, M 2021 “Living things are not (20th century) machines: Updating mechanism metaphors in light of the modern science of machine behavior”, *Frontiers in Ecology and Evolution*, vol. 9, art. 650726.

Buganim, Y, Faddah, DA, Cheng, AW, Itskovich, E, Markoulaki, S, Ganz, K, Klemm, SL, van Oudenaarden, A, & Jaenisch, R 2012 “Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase”, *Cell*, vol. 150, no. 6, pp. 1209–1222.

Buss, L 1987 *The evolution of individuality*, Princeton:
Princeton University Press.

Darwin, C 1859 *On the origins of species*, London: Murray.

Di Carlo, D, & Lee, LP 2006 “Dynamic single-cell analysis for quantitative biology”, *Analytical Chemistry*, vol. 78, no. 23, pp. 7918–7925.

Dobzhansky, T 1964 “Biology, molecular and organismic”, *American Zoologist*, vol. 4, no. 4, pp. 443–452.

Dussiau, C, Boussaroque, A, Gaillard, M, Bravetti, C, Zaroili, L, Knosp, C, Friedrich, C, Asquier, P, Willems, L, Quint, L, Bouscary, D, Fontenay, M, Espinasse, T, Plesa, A, Sujobert, P, Gandrillon, O, & Kosmider, O 2022 “Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level”, *BMC Biology*, vol. 20, no. 1, p. 60.

de Beco, S, Ziosi, M, & Johnston, LA 2012 “New frontiers in cell competition”, *Developmental Dynamics*, vol. 241, no. 5, pp. 831–841.
Elmentaite, R, Domínguez Conde, C, Yang, L, & Teichmann, SA 2022 "Single-cell atlases: Shared and tissue-specific cell types across human organs", *Nature Reviews Genetics*, vol. 23, pp. 395-410.

Ellis, RJ 2001 "Macromolecular crowding: Obvious but underappreciated", *Trends in Biochemical Sciences*, vol. 26, no. 10, pp. 597-604.

Elowitz, MB, Levine, AJ, Siggia, ED, & Swain, PS 2002 "Stochastic gene expression in a single cell", *Science*, vol. 297, 1183-1186.

Gawad C, Koh W, Quake, SR 2016 "Single-cell genome sequencing: Current state of the science", *Nature Reviews Genetics*, vol. 17, no. 3, pp. 175-188.

Guptasarma, P 1995 "Does replication-induced transcription regulate synthesis of the myriad low copy number proteins of Escherichia coli?", *Bioessays*, vol. 17, pp. 987-997.

Heams, T 2004 *Approche endodarwinienne de la variabilité intercellulaire de l'expression génétique*, PhD Dissertation, Institut National Agronomique Paris-Grignon, Paris.

Heams, T 2012 "Selection within organisms in the nineteenth century: Wilhelm Roux's complex legacy", *Progress in Biophysics and Molecular Biology*, vol. 110, no. 1, pp. 24-33.

Heams, T 2014 "Randomness in biology", *Mathematical Structures in Computer Science*, vol. 24, no. 3.

Kehr, J 2003 "Single cell technology" *Current Opinion in Plant Biology*, vol. 6, no. 6, pp. 617-621.

Kolodziejczyk, AA, Kim, JK, Svensson, V, Marioni, JC, & Teichmann, SA 2015 "The technology and biology of single-cell RNA sequencing", *Molecular Cell*, vol. 58, no. 6, pp 610-620.

Kumazaki, T, Hamada, K, & Mitsui, Y 1994 "Detection of mRNA expression in a single cell by direct RT-PCR", *Biotechniques*, vol. 16, no. 6, pp. 1017-1019.

Kupiec, JJ 1981 "Théorie probabiliste de la différenciation cellulaire", *XIIème rencontre de Méribel*, pp. 161-163.

Kupiec, JJ 1983 "A probabilist theory for cell differentiation, embryonic mortality and DNA C-value paradox", *Speculations in Science and Technology*, vol. 6, pp. 471-478.

Kupiec, JJ 2009 *The origin of individuals*, Singapore: World Scientific.

Lehner, B 2008 "Selection to minimise noise in living systems and its implications for the evolution of gene expression", *Molecular Systems Biology*, vol. 4, p. 170.

Lestas, I, Vinnicombe, G, & Paulsson, J 2010 "Fundamental limits on the suppression of molecular fluctuations", *Nature*, vol. 467, no. 7312, pp. 174-178.

Liang, SB, & Fu, LW 2017 "Application of single-cell technology in cancer research", *Biotechnology Advances*, vol. 35, no. 4, pp. 443-449.

Longo, D, & Hasty, J 2006 "Dynamics of single-cell gene expression", *Molecular Systems Biology*, vol. 2, p. 64.

McAdams, HH, & Arkin, A 1999 "It's a noisy business! Genetic regulation at the nanomolar scale", *Trends in Genetics*, vol. 15, no. 2, pp. 65-69.

Michod, RE 1996 "Cooperation and conflict in the evolution of individuality. II. Conflict mediation", *Proceedings of the Royal Society of London. Series B: Biological Sciences*, vol. 263, no. 1372, pp. 813-822.

Nicholson, D 2013 "Organisms ≠ Machines", *Studies in History and Philosophy of Biology and Biomedical Sciences*, vol. 44, no. 4, pt. B, pp. 669-678.

Noble, D 2006 *The music of life*, Oxford: Oxford University Press.

Ogawa, H, Horitani, K, Izumiya, Y, & Sano, S. 2022 "Somatic mosaicism in biology and disease", *Annual Review of Physiology*, vol. 84, pp. 113-133.

O'Huallachain, M, Karczewski, KJ, Weissman, SM, Urban, AE, & Snyder, MP 2012 "Extensive genetic variation in somatic human tissues", *Proceedings of the National Academy of Science of the United States of America*, vol. 109, no. 44, pp. 18018-23.

Parmentier, R, Racine, L, Moussy, A, Chantalat, S, Sudharshan, R, Papili Gao, N, Stockholm, D, Corre, G, Fourel, G, Deleuze, JF, Gunawan, R, & Paldi A 2022 "Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells", *PLoS Biology*, vol. 20, no. 10, art. e3001849.

Pearson, H 2008 "Cell biology: The cellular hullabaloo", *Nature*, vol. 453, pp. 150-153.

Raj, A, van Oudenaarden, A 2008 "Nature, nurture, or chance: Stochastic gene expression and its consequences", *Cell*, vol. 135, no. 2, pp. 216-226.

Roux, W 1881 *Der Kampf der Theile im Organismus* [The Struggle of the Parts in the Organism]. Leipzig: Engelmann.

Sood, V, & Misteli, T 2022 "The stochastic nature of genome organization and function", *Current Opinion in Genetics & Development*, vol. 72, pp. 45-52.

Spudich, JL, & Koshland, DE 1976 "Non-genetic individuality: Chance in the single cell", *Nature*, vol. 262, no. 5568, pp. 467-471.

Templer, RH, & Ces, O 2008 "New frontiers in single-cell analysis", *Journal of the Royal Society Interface*, vol. 5, suppl. 2, pp. S111-2.

Wang, D, & Bodovitz, S. 2010 "Single cell analysis: The new frontier in 'omics", *Trends in Biotechnology*, vol. 28, no. 6, pp. 281-290.

Wernet, M, Mazzoni, O, Çelik, A, Duncan, D, Duncan, I, & Desplan, C 2006 "Stochastic spineless expression creates the retinal mosaic for colour vision", *Nature*, vol. 440, no. 7081, pp. 174-180.

Wijgerde, M, Grosveld, F, & Fraser, P 1995 "Transcription complex stability and chromatin dynamics in vivo", *Nature*, vol. 377, pp. 209-213.