# CoLoC-seq probes the global topology of organelle transcriptomes

Damien Jeandard, Anna Smirnova, Akinyemi Mandela Fasemore, Léna Coudray, Nina Entelis, Konrad U F Örstner, Ivan Tarassov, Alexandre Smirnov

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Damien Jeandard, Anna Smirnova, Akinyemi Mandela Fasemore, Léna Coudray, Nina Entelis, et al. CoLoC-seq probes the global topology of organelle transcriptomes. Nucleic Acids Research, 2022, 10.1093/nar/gkac1183. hal-03908037

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# CoLoC-seq probes the global topology of organelle transcriptomes

Damien Jeandard¹,†, Anna Smirnova¹,†, Akinyemi Mandela Fasemore², Léna Coudray¹, Nina Entelis¹, Konrad U. Förstner¹,³,⁴, Ivan Tarassov¹ and Alexandre Smirnov¹,⁵,*

¹UMR7156 – Génétique Moléculaire, Génomique, Microbiologie (GMGM), University of Strasbourg, CNRS, Strasbourg, F-67000, France, ²University of Würzburg, Würzburg, D-97080, Germany, ³ZB MED – Information Centre for Life Sciences, Cologne, D-50931, Germany, ⁴TH Köln – University of Applied Sciences, Faculty of Information Science and Communication Studies, Institute of Information Science, Cologne, D-50678, Germany and ⁵University of Strasbourg Institute for Advanced Study (USIAS), Strasbourg, F-67000, France

Received October 14, 2022; Editorial Decision November 23, 2022; Accepted November 28, 2022

## ABSTRACT

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from numerous false-positives since incompletely digested contaminant RNAs can still be captured and erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat. CoLoC-seq leverages classical enzymatic kinetics and tracks the depletion dynamics of transcripts in a gradient of an exogenously added RNase, with or without organellar membranes. By means of straightforward mathematical modelling, CoLoC-seq infers the localisation topology of RNAs and robustly distinguishes between genuinely resident, luminal transcripts and merely abundant surface-attached contaminants. Our generic approach performed well on human mitochondria and is in principle applicable to other membrane-bounded organelles, including plastids, compartments of the vacuolar system, extracellular vesicles, and viral particles.

## INTRODUCTION

Gene expression is organized both temporally and spatially. This is especially striking in eukaryotic cells that possess several coexisting genetic systems and a developed intracellular network of membrane-bounded and membrane-less compartments. The genetic systems of the nucleus and of the genome-containing organelles (mitochondria and plastids), albeit mechanistically and functionally intertwined (e.g. via the inward protein import into the organelles, the outward retrograde signalling to the nucleus, and concerted translation programmes), remain largely segregated and tightly delimited by double membranes (1-3). Various steps of eukaryotic RNA biogenesis take place in distinct subcellular locales, including specialized nuclear condensates (such as nucleolus and Cajal bodies), cytoplasmic foci (where some snRNPs get assembled), and even the mitochondrial surface (where tRNA and piRNA maturation occurs in some species) (4-7). Mature transcripts are subject to further subcellular partitioning that brings them to their site of action, ensures their local translation, temporary sequestration, or turnover (8-10). This is achieved with the help of physical barriers (membranes, interphases), intracellular gradients of trans-acting factors (i.e. RNA-binding proteins, lncRNAs, ribosomes, degradation machineries), through molecular tethering of passively diffusing ribonucleoproteins (RNPs), or by their active, directed trafficking to specific subcellular locations (11,12). The importance of such an intricate organization of gene expression is highlighted by a growing number of human diseases associated with deregulations in the RNA localisation pathways (12-15). Beyond the cell limits, selective packaging of cellular RNAs into extracellular vesicles and viral particles has similarly attracted great attention from both the fundamental and applied points of view (16-26).

Understanding the general principles and the scope of RNA localisation comes by systematic, genome-wide stud-

<footnote>*To whom correspondence should be addressed. Tel: +33 368851812; Email: alexandresmirnov@unistra.fr</footnote>

<footnote>†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.</footnote>
ies. These have become possible thanks to recent advances in
high-throughput methods aiming at comprehensive profil-
ing of subcellular and extracellular transcriptomes (27,28).
While some of these techniques use fluorescence *in situ*
RNA hybridization to visualise selected individual tran-
scripts (29–31), most others employ RNA-seq as a sensitive
and relatively unbiased tool to identify and quantify poten-
tially all RNAs associated with the compartment in ques-
tion (32,33). The earliest and conceptually simplest method
of this latter group includes a fractionation step whereby
the organelle of interest (or a viral particle) is first iso-
lated, usually treated with an RNase and/or other agents
to remove co-purified, surface-attached contaminants, and
analysed for its RNA content by northern blotting, RT-
qPCR or deep sequencing (21–25,34–42). More recently,
proximity labelling techniques, such as APEX-RIP, APEX-
Seq, Proximity-CLIP, Cap-seq and proximity-specific ri-
bosome profiling, enabled high-resolution *in situ* subcel-
lular RNA profiling based on the deposition of covalent
tags on RNPs found in immediate vicinity of a geneti-
cally engineered enzyme targeted to the organelle of inter-
est. These powerful methods have drawn first nearly com-
prehensive RNA landscapes of mitochondria, endoplasmic
reticulum, nucleus, cytosol and even distinct subnuclear ter-
ritories (43–50). In many cases, they also provided access
to the localisation topology of organelle-associated tran-
scripts by distinguishing internal RNAs, present in the lu-
minal space of organelles, and external, surface-attached
RNAs. This property is biologically very important as it
determines which transcripts and proteins can in principle
interact in the cellular milieu, eventually producing physio-
logically relevant outcomes, and which should never come
in touch with each other.

Each of the above approaches has its strengths and
weaknesses (33,51). Fractionation-based techniques per-
form well on most membrane-bounded organelles and even
on some viruses with robust, RNase-proof proteinaceous
capsids (52–54). They produce high amounts of analysable
material and do not require any genetic modification of the
studied system, which facilitates their implementation in ge-
netically intractable organisms and tissues. On the other
hand, with the advent of RNA-seq they increasingly fall
prey to the high sensitivity of this technique, which can de-
tect even low-level contaminating RNA species and falsely
declare them as resident transcripts. By contrast, proximity
approaches identify organellar RNAs with exquisite spatial
precision and even perform reasonably well on membrane-
less compartments which do not tolerate RNase treatment
(46,55). However, proximity labelling techniques require ge-
netic introduction of the tagging enzyme and its specific,
infallible localisation to the compartment of interest. This
restricts their use to those model species for which appropri-
ate genetic engineering tools and good knowledge of protein
localisation pathways are available (so-far they have been
used essentially in cultured human and yeast cells). Further-
more, biochemical tagging is usually a low-yield, random
process, and the existing proximity approaches typically fo-
cus on long polyadenylated transcripts, which biases them
against shorter, noncoding, or less abundant RNA species
(45,49,50). Therefore, although the introduction of these
methods was truly transformative for the field of subcellu-

lar localisation, more comprehensive, unbiased, and widely
applicable subcellular transcriptomic tools are in demand to
complement the existing techniques and extend their scope
to non-model organisms and less tractable compartments.

Here, we describe an alternative organelle transcrip-
tomics approach that preserves the key advantages of
fractionation-based techniques while alleviating their
main shortcoming – poor control over rampant false-
positives. This generic method, called Controlled Level
of Contamination coupled to deep sequencing (CoLoC-
seq), instead of aspiring to fully decontaminate purified
organelles (which is practically unfeasible), leverages the
basic enzymatic kinetics of RNase digestion and infers the
localisation topology of transcripts from their biochemical
reactivity across a gradient of RNase concentrations
(Figure 1). Exposing organelle-associated transcripts to
an added RNase creates unique, RNA-specific depletion
dynamics, which depends on the size of the RNA pool
available to degradation and on the cleavage rate constant.
The ensemble of these degradation kinetics is analysed
transcriptome-wide with a dedicated RNA-seq pipeline,
and a simple kinetics model is then fitted to measure the
reactivity and the localisation topology of each RNA.
Under this setup, contaminant RNAs follow gradual,
pseudo-first order depletion dynamics, which betrays them
even when they cannot be completely digested within
the allotted time. By contrast, resident transcripts show
a plateau equivalent to the pool of molecules protected
from the RNase by the organellar membranes. Thus,
regardless of the absolute numbers of reads mapping to
each transcript from the RNase-treated organelles, one can
distinguish between bona fide resident (luminal) RNAs and
merely abundant surface-attached contaminants. We ap-
plied this method to human mitochondria and successfully
detected all long and the majority of short mitochondrial
transcripts as genuinely resident, validating our strategy.
Moreover, CoLoC-seq confidently classified all long and
most smaller nuclear-encoded RNA species associated with
mitochondria as contaminants (even when they remained
numerically abundant in the ‘purified’ organelles), while
providing evidence for partial mitochondrial localisation
of a small subset of RNA polymerase III-generated tran-
scripts, including Y RNAs and select tRNAs. The generic
nature of CoLoC-seq makes it in principle applicable to
other membrane-bounded organelles, extracellular vesi-
cles, and virions, independently of their origin and prior
knowledge.

MATERIALS AND METHODS

Reagents

Commercial enzymes and other recombinant proteins used in
this study. DNase I (Thermo Fisher Scientific, Waltham,
USA, Cat # EN0525), Micrococcal nuclease (Thermo
Fisher Scientific, Waltham, USA, Cat # 88216), RNA
5'-pyrophosphohydrolase (New England Biolabs, Ipswich,
USA, Cat # M0356S), RNase A (Thermo Fisher Scien-
tific, Waltham, USA, Cat # EN0531), RNase A/T₁ mix (2
mg/ml, 5,000 U/ml; Thermo Fisher Scientific, Waltham,
USA, Cat # EN0551), RNase One (Promega, Madison,
USA, Cat# M4261), RNase T₁ (Thermo Fisher Scientific,
<figure><img src="image_3.png" /><figcaption>**Figure 1.** CoLoC-seq pipeline. The topology of a subcellular transcriptome, associated with an organelle of interest, can be interrogated by performing two types of controlled, kinetically resolved RNase treatment reactions. To this end, crude organelles (e.g. mitochondria) are isolated from a cell culture or a tissue and divided in two portions to follow either the CoLoC-seq or the Mock CoLoC-seq protocol. In the CoLoC-seq setup (top), a preparation of intact organelles is split in a series of identical samples subjected, during a given time, to a gradient of RNase A concentrations (including an untreated sample). This creates characteristic digestion dynamics which, for each RNA species, depends on its exact location and its accessibility to the RNase. These dynamics can be analysed transcriptome-wide by deep sequencing of remaining intact RNA in each sample, followed by standard kinetics mathematical modelling to infer the reaction rate and the protection status of individual RNA species (see Materials and Methods for model details). Resident transcripts (blue), shielded by the organellar membranes, do not participate in the reaction and remain at a constant level. By contrast, typical RNase-sensitive surface-exposed contaminants (green) follow a gradual, pseudo-first order decay. Partially resident transcripts (violet) combine the two behaviours by producing an intermediate-level plateau. In the Mock CoLoC-seq protocol (bottom), the preparation of organelles is first mildly solubilised to destroy membranes and only then subjected to a similar gradient of RNase A concentrations as in CoLoC-seq. In this case, all transcripts should in principle be accessible to the RNase and take part in the reaction, yielding a pseudo-first order decay curve. However, if an RNA species is intrinsically RNase-resistant (e.g. due to its tight structure or its protection by proteins), it will plateau in both CoLoC-seq and Mock CoLoC-seq experiments, independently of the membrane integrity (red).</figcaption></figure>

Waltham, USA, Cat # EN0541), SUPERase-In RNase Inhibitor (Thermo Fisher Scientific, Waltham, USA, Cat # AM2694).

*Kits and other reagents used in this study.* AMPure XP kit (Beckman Coulter, Indianapolis, USA, A63881), Bradford assay ROTI Nanoquant (Carl Roth, Karlsruhe, Germany, Cat # K880.1), Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, USA, Cat # 11668019), ProLong Gold Antifade Mountant (Thermo Fisher Scientific, Waltham, USA, Cat # P36930), Protein A-Sepharose 6MB (Sigma-Aldrich, Saint Louis, USA, Cat # P6649), Roti Aqua-

P/C/I (Carl Roth, Karlsruhe, Germany, Cat # X985.1), SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific, Waltham, USA, Cat # 34579), TRIzol reagent (Thermo Fisher Scientific, Waltham, USA, Cat # 15596026), ViewRNA ISH Cell assay kit (Thermo Fisher Scientific, Waltham, USA, Cat # QVC0001).

*Antibodies used in this study.* mouse monoclonal α-CYCS (BD Pharmingen, Franklin Lakes, USA, Cat # 556432), goat polyclonal α-ENO (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-7455), mouse monoclonal α-His (Sigma-Aldrich, Saint Louis, USA, Cat # SAB1305538),
mouse monoclonal α-LRPRC (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-166178), rabbit polyclonal α-OPA1 (Proteintech Group, Rosemont, USA, Cat # 27733-1-AP), rabbit polyclonal α-PNPase (Abcam, Cambridge, UK, Cat # ab96176), mouse monoclonal α-Ro60 (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-100844), rabbit polyclonal α-Ro60 (Sigma-Aldrich, Saint Louis, USA, Cat # HPA002835), rabbit polyclonal α-TOMM20 (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-11415), rabbit polyclonal α-uL4m (Protein-tech Group, Rosemont, USA, Cat # 27484-1-AP), polyclonal sheep HRP-conjugated α-mouse (GE Healthcare, Chicago, USA, Cat # NXA931), polyclonal donkey HRP-conjugated α-rabbit (GE Healthcare, Chicago, USA, Cat # NA934V).

*Specialised commercial instruments used in this study.*
ChemiDoc Touch Imaging System (Bio-Rad, Hercules, USA, Cat # 1708370), LSM 780 microscope (Carl Zeiss, Oberkochen, Germany), NextSeq 500 (Illumina, San Diego, USA), Optima XPN-100 ultracentrifuge (Beckman Coulter, Brea, USA), Typhoon Trio Variable Mode Imager System (GE Healthcare, Chicago, USA).

## Biological resources

*Cell lines used in this study.* Flp-In T-REx 293 cell line (Thermo Fisher Scientific, Waltham, USA, Cat # R78007), SAL004 (Flp-In T-REx 293-derived PDHA1-mCherry-expressing; see section ‘Creation of a PDHA1-mCherry-expressing cell line’).

*Plasmids used in this study.* mCherry-PDHA1-N-10 (Addgene, Cambridge, USA, Cat # 55118), pcDNA5 FRT/TO (Thermo Fisher Scientific, Waltham, USA, Cat # V652020), pOG44 (Thermo Fisher Scientific, Waltham, USA, Cat # V600520), ASP0042 (derived from mCherry-PDHA1-N-10 and pcDNA5 FRT/TO).

## Cell culturing

Human Flp-In T-REx 293 cells, derived from the HEK293 cell line, were cultivated at 37°C in the presence of 5% CO₂ in Eagle's Minimum Essential Medium (EMEM, Sigma) containing 1 g/l glucose and supplemented with 1.5 g/l sodium bicarbonate and 0.11 g/l sodium pyruvate. One complete set of CoLoC-seq and Mock CoLoC-seq samples requires ~2500 cm² of nearly confluent cells (equivalent of ~3 × 10⁸ HEK293 cells). All cultures were routinely verified by PCR for mycoplasma contamination, as described (56). At 80% confluence, the cells were harvested by incubation with 2.5 mM EDTA in PBS for 20 min at 37°C, centrifuged at 600 g for 10 min at room temperature, and washed once with PBS.

HepG2 and SAL004 cells for microscopy experiments were cultured in standard Dulbecco's modified Eagle's medium (DMEM) containing 4.5 g/l glucose, supplemented with 10% fetal bovine serum, Penicillin-Streptomycin (Sigma-Aldrich) and Amphotericin B (Sigma-Aldrich), at 37°C, 5% CO₂.

## Creation of a stable PDHA1-mCherry-expressing cell line

A gene for pyruvate dehydrogenase E1 subunit alpha 1 fused N-terminally to mCherry (PDHA1-mCherry) from the plasmid mCherry-PDHA1-N-10 was amplified with primers Anja0062 and Anja0063 (Supplementary Table S1) and recloned into pcDNA5 FRT/TO. The resulting plasmid is referred to as ASP0042. For creation of a stable cell line, Flp-In T-REx 293 cells were transfected with a mixture of 0.4 µg of ASP0042 and 3.6 µg of pOG44 (encoding Flp-recombinase) with 10 µl of Lipofectamine 2000 in 2 ml of OptiMEM (Gibco) in 9 cm² Petri dishes. After 6 h of transfection, the medium was changed to EMEM and the cells were left to propagate for 48 h. Then the cells were trypsinized, diluted, and reseeded in the presence of 320 µg/ml hygromycin B Gold (InvivoGen). In 48 h, concentration of hygromycin was reduced to 160 µg/ml, and 18 days after transfection, a population of clones was isolated and propagated in the presence of 160 µg/ml hygromycin. The resulting cell line used in this study is referred to as SAL004. PDHA1-mCherry expression was induced with 0.2 mg/ml tetracycline for 24 h.

## Preparation of crude mitochondria

Crude mitochondria were isolated by differential centrifugation, following established protocols (57–60), with modifications. All procedures were carried out at 4°C, unless specified differently. Harvested cells were resuspended in 30 ml of ice-cold Buffer A (10 mM Tris-HCl, pH 6.7, 0.6 M sorbitol) and disrupted at high speed with a pre-chilled laboratory blender (Waring Commercial) three times for 15 s (with 1–2 min intervals to avoid overheating). Cellular debris and nuclei were removed by three 3-min centrifugations at 1000 g, the supernatant being every time transferred into a fresh tube. The mitochondria were then pelleted by centrifugation at 21 000 g for 30 min. The crude mitochondrial pellet was carefully but thoroughly resuspended in 8 ml of Buffer A and split in four 2-ml portions. Each portion was loaded on a two-cushion sucrose gradient formed of 10 ml of Buffer B (10 mM Tris-HCl, pH 6.7, 1.65 M sucrose) and 15 ml of Buffer C (10 mM Tris-HCl, pH 6.7, 0.6 M sucrose) in SW 32 Ti tubes (Beckman Coulter). Upon 1-h centrifugation at 45 000 g, the mitochondria-containing interphase was collected. The interphases were washed with excess of Buffer A and centrifuged at 21 000 g for 30 min. The supernatant was discarded.

## CoLoC procedure

The mitochondrial pellets were combined and gently but thoroughly resuspended in 800 µl of Buffer A. 500 µl of the suspension were saved for a regular CoLoC-seq experiment, whereas the remaining 300 µl were reserved for the accompanying mock CoLoC-seq experiment. The concentration of the first portion was adjusted with Buffer A to ~1.6 mg/ml of mitochondrial protein (as measured by Bradford assay). The resulting suspension was split in a series of identical 80-µl samples (normally, ~10 samples must be sufficient to create an informative depletion curve). In parallel, a series of 80-µl RNase A dilutions in Buffer D (10 mM Tris-HCl, pH 6.7, 0.6 M sorbitol, 200 mM NaCl,
2 mM EDTA) were prepared with concentrations ranging from 0 to 6 µg/ml. For the experiments described in this paper, the following concentrations have been applied: CoLoC-seq #1 – 0, 0.2, 0.6, 1.2, 2.0, 2.6, 3.2, 4.0 and 6.0 µg/ml; CoLoC-seq #2 – 0, 0.1, 0.2, 0.6, 1.2, 2.0, 2.6, 3.2, 4.0 and 6.0 µg/ml. The mitochondrial suspensions and the RNase A dilutions (pre-warmed to 25°C for 1 min) were mixed 1:1 and incubated for 10 min on a water bath at 25°C. The reactions were then diluted with 1.6 ml of pre-chilled Buffer E (10 mM Tris–HCl, pH 6.7, 0.6 M sorbitol, 5 mM EDTA) and centrifuged at 16 000 g for 20 min. The pellet was thoroughly resuspended in 100 µl of Buffer F (10 mM Tris–HCl, pH 6.7, 0.6 M sorbitol, 1 mM EDTA), and RNA was extracted with TRIzol (Invitrogen), following the manufacturer's instructions. Each extracted RNA sample was mixed with the identical amount (150 ng for CoLoC-seq #1, 90 ng for all other experiments) of a yeast tRNA-sgRNA chimeric spike-in transcript (5'-GAGAAGUAAGCACUGUAAAGGUUU UAGAGCUAGAAAUAGCAAGUAAAAAUAAGG CUAGUCCGUUAUCAACUUGAAAAAGUGGCA CCGAGUCGGUGCUUGCCUUGUUGGCGCAAU CGGUAGCGCGUAUGACUCUUAAUCAUAAGG UUAGGGGUUCGAGCCCCCUACAGGGCUCCA), which does not cross-map to the human genome (61), to enable data normalization. The samples were treated with 1 U of DNase I in the presence of 20 U of SUPERase-In, re-extracted with TRIzol, and stored at -80°C.

**Technical note**

There are two important considerations to take into account when choosing the RNase for CoLoC-seq experiments: (i) it should have properties of a kinetically perfect enzyme to enable straightforward data analysis (see the section 'Kinetics model derivation and fitting') and (ii) it should generate 5'-hydroxyl and 2'/3'-phosphate termini to permit selective sequencing of intact transcripts (see the section 'Library preparation and RNA-seq'). Other RNases creating 5'-hydroxyl and 2'/3'-phosphate ends (micrococcal nuclease, RNase I, RNase T₁) had been tested as well but were found to be overall less active and more idiosyncratic than RNase A (Supplementary Figure S1), in agreement with previous reports (62,63). The choice of RNase concentrations depends on the specific activity of the enzyme batch and the nature of the material to treat and should be adjusted individually for every new application. It must enable the observation of a gradual digestion dynamics of contaminant transcripts without compromising the quality of the final RNA samples.

**Mock CoLoC procedure**

For the mock CoLoC-seq experiment, the 300 µl of crude mitochondrial suspension were mixed with 300 µl of ice-cold Buffer H (10 mM Tris-HCl, pH 6.7, 0.6 M sorbitol, 1% n-dodecyl-β-D-maltoside) and lysed on ice with a small Dounce homogenizer (50 strokes). The lysate was cleared by two 20-min centrifugation at 16 000 g, at 4°C. The cleared lysate was adjusted to ~1.6 mg/ml of protein (as measured by Bradford assay) with Buffer A and split in a series if identical 80-µl samples. The RNase A treatment was performed

in the same way as in the standard CoLoC procedure. The following RNase A concentrations (before mixing with the mitochondrial lysate) have been used for the mock CoLoC-seq #1 – 0, 0.2, 0.6, 1.2, 2.0, 2.6, 4.0, 4.6, 5.2 and 6.0 µg/ml; for the mock CoLoC-seq #2 – 0, 0.1, 0.2, 0.6, 1.2, 2.0, 2.6, 3.2, 4.0 and 6.0 µg/ml. After the 10-min incubation at 25°C, the reactions were centrifuged for 40 min at 16 000 g at 4°C (to subject them to conditions similar to the regular CoLoC procedure). RNA was then extracted with TRIzol and stored, as described above.

**Northern blotting**

Northern blotting was performed as described (64). For this, ~1 µg of the untreated ('0') RNA samples were mixed 1:1 with 0.025% SDS, 18 mM EDTA, 0.025% bromophenol blue in deionised formamide, denatured for 5 min at 95°C, and loaded onto a 6% gel in 1× TBE with 8 M urea. All the other, RNase-treated, samples were loaded in the same volume fraction to enable their direct comparison. Upon migration and ethidium bromide staining, RNA was transferred onto a nylon Amersham HybondN+ membrane (GE Healthcare) and UV-crosslinked. Pre-hybridization was carried out in 6× SSC containing 5× Denhardt's solution and 0.2% SDS at 65°C for 30 min. To probe for specific transcripts, the membrane was incubated overnight with continuous rotation with a 5'-[³²P]-labelled oligonucleotide probe (see Supplementary Table S1) in 3× SSC, 0.1% SDS, 0.5× TE, 0.5 M NaCl, 5× Denhardt's solution at 42°C. After hybridization, the membrane was washed with 5× SSC, 0.1% SDS, dried, and exposed with a Phosphorimager plate. The radioactive signal was visualised on Typhoon Trio (GE Healthcare) and analysed with ImageQuant TL (v. 7.0, GE Healthcare). The spike-in RNA signal was used for normalization across the entire sample range and the reconstruction of depletion dynamics for each transcript. To re-probe for a different RNA, membranes were stripped in stripping buffer (1% SDS, 0.1× SSC, 40 mM Tris-HCl, pH 7.6) at 80°C three times for 10 min and washed once in 2× SSC.

**Library preparation and RNA-seq**

cDNA library preparation and RNA-seq were performed by Vertis Biotechnologie AG (Freising, Germany). Supplementary Figure S2 shows the key library preparation steps that ensure the selective sequencing of intact transcripts. Briefly, cap structures were first removed with RNA 5'-pyrophosphohydrolase. The 5'-adapter was ligated to 5'-phosphorylated ends. Then the 3'-adapter was ligated to 3'-hydroxyl ends. The first-strand cDNA synthesis was performed with M-MLV reverse transcriptase with a 3'-adapter-annealing primer. The resulting cDNA was PCR-amplified (15 cycles) with a high-fidelity DNA polymerase and barcoded TruSeq primers (Supplementary Table S1). cDNA was purified with the AMPure XP kit, fragmented, end-repaired, and subjected to another round of adapter ligation and PCR amplification. The cDNA samples were pooled equimolarly and size-selected on an agarose gel in the range 10-to-220 nt (excluding the flanking sequences). The pool was sequenced on an Illumina NextSeq 500 instrument (75-nt single-end reads).
Although the fragmentation step had been expected to destroy the strand-specificity of the protocol, in reality, mapping results showed the sequencing reads to cluster on the 5'-end of the annotated genes in sense orientation (Supplementary Figures S3 and S4). Therefore, the initial 5'-adapter ligation largely determined the strandedness of the reads, which permitted to exclude eventual ambiguity in transcript assignment and quantification.

## CoLoC-seq data processing

Sequencing read libraries were pre-processed with cutadapt version 2.8 (65) to trim adapter sequences. Thereafter, read alignment and gene feature quantification was done with READemption version 0.4.3 (66) (https://doi.org/10.5281/zenodo.250598). All libraries were aligned to the Human genome (Genome Reference Consortium Human Build 38 patch release 13) retrieved from RefSeq (67). READemption uses segemehl version 0.2.0–418 (68) as the read aligner. The options used for alignment, coverage calculation, and feature quantification can be found in the scripts deposited at Zenodo (https://doi.org/10.5281/zenodo.6389451).

Entries for repetitive genes were compounded, and their reads were summed up. Genes for which the majority of reads came from cross-mapping to embedded tRNA-, snRNA-, 7SL-, 5S rRNA- or mtDNA-like (NUMTs) sequences were excluded from the analysis. A cut-off of 30 reads was applied to all untreated samples to ensure robust starting level measurement (smaller cut-offs resulted in the inclusion of lower-quality, noisy profiles, and were, therefore, abandoned). Read counts in each library were normalised by the corresponding number of reads mapping to the spike-in RNA to reconstruct digestion profiles. The RNA level in the untreated samples (0 µg/ml RNase A) was set to 1. Read distributions across select genomic loci were visualised in the Integrated Genome Browser (v. 9.1.8).

## Kinetics model derivation and fitting

Classical degradation of an ith transcript by a kinetically perfect enzyme (whose catalysis rate is only limited by substrate binding and not by the chemical reaction itself), such as RNase A (69), is described by the standard equation:

$$ \frac{dR_i}{dt} = -k_i R_i A $$

where $R_i$ is the concentration of the ith RNA, A is the RNase A concentration (assumed to remain constant during digestion), and $k_i$ is the corresponding pseudo-first order rate constant.

Separation of variables and integration by time results in the canonical exponential decay function:

$$ R_i = R_0 e^{-k_i A t} $$

where $R_0$ and $R_i$ are the concentrations of the ith RNA at the time points 0 and t.

The ratio $f(A) = R_i/R_0$ is experimentally measurable and represents the proportion of intact ith RNA remaining after digestion with the Aith concentration of RNase A at the time point t. However, if the reaction system contains a pool of

ith transcript of the size r, which does not take part in the reaction (e.g. protected by organellar membranes), this ratio will assume a slightly different form:

$$ f(A) = \frac{R_i + r}{R_0 + r} = \frac{R_0 e^{-k_i A t} + r}{R_0 + r} = \frac{R_0}{R_0 + r} e^{-k_i A t} + \frac{r}{R_0 + r} = (1 - P_0) e^{-k_i A t} + P_0 $$

where the constant $P_0 = \frac{r}{R_0+r}$ corresponds to the initial proportion of the ith RNA that is in principle unavailable for digestion by RNase.

If one fixes time (e.g. to t = 10 min, as in this study), the model will have only two parameters to be determined, based on experimentally measured remaining proportion f(A) versus RNase A concentration A:

$$ f(A) = (1 - P_0) e^{-k_i A} + P_0 \quad (1) $$

where $k'_i$ reflects the rate with which RNase A digests the ith transcript, and $P_0$ measures the relative size of the protected pool, which is the parameter of interest of CoLoC-seq.

Remark that when an RNA is not localised inside the organelle, i.e. its $P_0 = 0$, the equation resolves back into the canonical pseudo-first-order decay:

$$ f(A) = e^{-k'_i A} \quad (2) $$

By contrast, transcripts fully localised inside the organelle and inaccessible for RNase A ($P_0 = 1$) are expected to show a constant level independent of RNase A concentration:

$$ f(A) = P_0 = 1 \quad (3) $$

Nonlinear curve fitting of Model 1 into CoLoC-seq and Mock-CoLoC-seq data (for either individual or combined replicates) was performed in Origin 2021b (v9.8.5.212, OriginLab Corporation) with the following settings. Constraints on parameters: $0 \le P_0 \le 1$, $k'_i \ge 0$; Leverberg Marquardt regression method; initial $P_0$ estimate fixed to the lowest observed f(A) value; 500 iterations; tolerance 1E-09; ‘Model comparison method’ for CI computation for the parameters. In a few cases where the fit did not converge or the dependency between parameters exceeded 0.3 (which precludes their reliable determination and suggests that the model is unnecessarily complex), the simpler Model 2 was fitted without constraints. High and significant $P_0$ values (Model 1) or small and/or insignificant $k'_i$ values (both models) indicate that the transcript level is largely stable and insensitive to RNase A digestion. Note that the Model 3 is implicitly tested as alternative ($R^2 = 0$) in both Model 1 and Model 2.

## Submitochondrial fractionation for RNA and protein localisation

The submitochondrial fractionation was performed as in (64,70), with modifications to assess the RNA localisation. Flp-In T-REx 293 cells (225 cm²) were grown to 80–90% confluency, harvested, resuspended in 1.5 mL of ice-cold Buffer F (0.6 M sorbitol, 10 mM HEPES–KOH, pH 7.5,
1 mM EDTA), and disrupted on ice with a 2-ml syringe and a 26G × 25 mm needle by 20 strokes. The disrupted cells were centrifuged at 600 g for 10 min at 4°C, then at 1000 g for another 10 min to sediment nuclei and cell debris. The pre-cleared supernatant was then centrifuged at 14 000g for 20 min at 4°C to sediment mitochondria. The mitochondrial pellet was thoroughly resuspended in 500 µl of Buffer F, and its protein content was measured by Bradford assay. The mitochondrial suspension was then split in three aliquots of 167 µl, diluted with 1 ml of Buffer F to each new tube and centrifuged at 14 000 g for 10 min. The pellets were resuspended at ~2 mg of mitochondrial protein per ml in either of the three buffers: sample #1 in the isotonic Buffer F, sample # 2 in the hypotonic Buffer G (10 mM HEPES-KOH, pH 7.5, 1 mM EDTA), sample #3 in the lysis Buffer H (10 mM HEPES-KOH, pH 7.5, 1 mM EDTA, 0.5% n-dodecyl-β-maltoside). The latter sample was thoroughly lysed with a 2-ml syringe and a 26G × 25 mm needle by 10 strokes to ensure complete membrane solubilisation. Each of these three samples was then split in three identical aliquots, of which one was left untreated, one was added 1 µl of RNase A/T₁ mix (2 mg/ml RNase A, 5000 U/ml RNase T₁; Thermo Fisher Scientific), and one was added 5 µl of RNase A/T₁ mix. All aliquots were incubated at 25°C for 10 min. Then 1.5 ml of TRIzol reagent was added, and RNA was extracted following the manufacturer's instructions. All RNA samples were dissolved in 35 µl of RNase-free water, and 10 µl of each sample was resolved in denaturing 8 M urea 6% PAGE followed by northern blotting, as described above.

To assess the submitochondrial localisation of proteins, the three mitochondrial samples resuspended in Buffers F, G, H were each split in two aliquots. One of the aliquots was left untreated, whereas the other was added 50 µg/ml proteinase K. Both aliquots were incubated on ice for 20 min. Then 1 mM PMSF and 1/4 volume of 100% trichloroacetic acid (prepared by dissolving 5 g of dry TCA in 3.5 ml of water) were added to all the samples, mixed well, and incubated for another 10 min on ice to stop digestion and precipitate proteins. The digests were centrifuged for 10 min at 14 000 g at 4°C. The pellets were washed twice with ice-cold acetone, dried overnight on the bench, and resuspended in 100 µl of 1× Laemmli buffer. For analysis, the samples were incubated at 80°C for 5–10 min, briefly sonicated to disperse the pellets, and 10 µl of each sample was resolved by 10% (for large proteins) or 15% (for small proteins) SDS-PAGE, followed by semi-dry transfer onto an Amersham Protran blotting nitrocellulose membrane (GE Healthcare). The membrane was blocked with 1 × TBS containing 0.05% Tween-20 and 10% skimmed milk and incubated with antibodies against Ro60 and established markers of mitochondrial subcompartments in 1× TBS, 0.05% Tween-20 for 1 h at room temperature. After a wash with 1× TBS, 0.05% Tween-20 for 30 min, secondary HRP-conjugated antibodies were applied to the membrane in the same way, and the resulting chemiluminescent signals were visualised with the help of SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific) on ChemiDoc Touch Imaging System (Bio-rad), further analysed in Image Lab (v. 5.2.1).

## Coimmunoprecipitation of Ro60 complexes

Coimmunoprecipitation was performed as described in (64). Crude mitochondria were isolated from 450 cm² of Flp-In T-REx 293 cells grown to ~80% of confluence, as described in the section 'Submitochondrial fractionation for RNA and protein localisation'. The mitochondria were completely lysed by resuspension in 1 ml of Buffer I (20 mM Tris-HCl, pH7.5, 150 mM KCl, 1 mM MgCl₂, 1 mM DTT, 0.5% n-dodecyl-β-D-maltoside, 1 mM PMSF) and disruption with a small Dounce homogeniser (20 strokes). The lysate was cleared by centrifugation at 14 000 g for 10 min at 4°C. A 10-µl sample was collected and mixed with 90 µl of 1× Laemmli buffer. For RNA analysis, a 100-µl sample was mixed with 1 ml of TRIzol to extract RNA. The lysate was pre-treated by incubation with 75 µl (bed volume) of protein A-sepharose beads (Sigma-Aldrich, pre-washed 5 times with 1 ml of Buffer J: 20 mM Tris-HCl, pH7.5, 150 mM KCl, 1 mM MgCl₂, 1 mM DTT, 1 mM PMSF) at 4°C for 30 min to remove unspecifically interacting proteins. The pre-treated lysate was then split in two equal portions: one portion was added 30 µl of a control antibody (goat polyclonal α-ENO, Santa Cruz Biotechnology, Inc, Cat # sc-7455) and the other with 30 µl of mouse monoclonal α-Ro60 antibody (Santa Cruz Biotechnology, Inc., Cat # sc-100844). The samples were rocked for 30 min at 4°C, then each mixed with 75 µl (bed volume) of protein A-sepharose beads (pre-washed 5 times with 1 ml of Buffer J) and rocked for another 30 min at 4°C. The beads were pelleted by centrifugation at 14 000 g for 30 s. A 10-µl and a 100-µl samples were collected and treated as previously for subsequent protein and RNA analyses. The beads were washed with 5 × 1 ml of Buffer J, and the last wash samples were collected and treated in the same way. The beads were resuspended in 0.5 ml of Buffer J, added 0.5 ml of phenol:chloroform:isoamyl alcohol (P:C:I, 25:24:1, Carl Roth), shaken vigorously for 20 s, and left on the bench for 3 min to initiate phase separation. The samples were centrifuged at 15 200g for 30 min at 15°C. The aqueous phases were collected, added 50 µg/ml of glycogen, and RNA was precipitated with 750 µl of isopropanol overnight at -20°C. Precipitated RNA was pelleted by centrifugation at 15 200 g for 20 min at 4°C, washed with 80% and 100% ethanol, dried, and solubilised in 35 µl of RNase-free water. The organic phase and the interphase after the P:C:I extraction were thoroughly mixed with 1.5 ml of ice-cold acetone, and the proteins were precipitated overnight at -20°C. Precipitated proteins were pelleted by centrifugation at 15 200 g for 30 min at 4°C, washed twice with ice-cold acetone, dried on the bench for 8 h and solubilised in 150 µl of 1× Laemmli buffer. For RNA analysis, 5 µl of each RNA sample were resolved by a denaturing 6% PAGE followed by northern blotting, as described above. For protein analysis, all samples were boiled at 80°C for 5 min, and 20 µl of lysate, flow-through and wash samples were resolved along with 40 µl of coIP samples by 10% SDS-PAGE followed by western blotting, as described in the section 'Submitochondrial fractionation for RNA and protein localisation'.

Coimmunoprecipitation of Ro60 complexes from total cell lysates for validation of the mouse α-Ro60 antibody (Santa Cruz Biotechnology, Inc, Cat # sc-100844)
was performed similarly, with the same amount of start-
ing material and an α-His antibody (Sigma-Aldrich, Cat #
SAB1305538), which does not have antigens in the Flp-In
T-REx 293 cell line, as negative control. Coimmunoprecip-
itates were analysed by LC-MS/MS as described in (64).
The spectral counts for each protein detected at FDR <1%
in the α-Ro60 and α-His samples were normalised by the
total number of spectral counts and compared. Proteins en-
riched >2-fold in the α-Ro60 sample, with non-overlapping
95% Poissonian confidence intervals, were deemed signifi-
cantly enriched.

Immunofluorescence, single-molecule RNA FISH and mi-
croscopy

Immunofluorescence and smFISH were performed in HepG2 cells as described in (64,71). Briefly, cells were seeded on an 8-well Nunc Lab-Tek slide (Thermo Fisher Scientific) 24-to-48 h prior the experiment, then fixed by incubation with a 3% formaldehyde solution diluted with the DMEM medium (4% paraformaldehyde dissolved in PBS by heating at 60°C and adjusted to 3% with DMEM) for 12 min at 37°C. For immunostaining, the cells were permeabilised for 10 min at room temperature with 0.3% Triton X-100 solution in 1× PBS. After blocking at room temperature for 30 min with 5% bovine serum albumin (BSA) in 1× PBS, the samples were incubated for 1-to-3 h with primary antibodies diluted in the blocking buffer. They were then incubated with the corresponding secondary antibody conjugated with Alexa Fluor 488, 555 or 647. Each step alternated with five washes with 1× PBS. Branched DNA RNA smFISH combined with antibody-mediated protein detection (71,72) was performed with the ViewRNA ISH Cell assay kit (Thermo Fisher Scientific), following the manufacturer's protocol (see Supplementary Table S1 for further information about the probe sets used).

For mitoplast isolation, induced SAL004 cells, cultivated
in a 75 cm² flask, were detached with ice-cold PBS-EDTA,
centrifuged and resuspended in 1 ml of MitoBuffer (0.6 M
sorbitol, 1 mM EDTA, 10 mM Tris-HCl, pH 7.5) supple-
mented with 0.3% BSA. Cells were disrupted on ice with
a 2-ml syringe and a 26G × 25 mm needle by 20 strokes.
Cell debris and nuclei were removed by two 5-min centrifu-
gations at 1500 g at 4°C. The supernatant was centrifuged
for 5 min at 13 000 g at 4°C to pellet mitochondria. The
crude mitochondrial pellet was resuspended 300 µl of Mi-
toBuffer, and an equal volume of the 2× RNase A solu-
tion (10 µg/ml RNase A in Mitobuffer supplemented with
4 mM MgCl₂) was added. After incubation for 7 min at
room temperature, 800 µl of MitoBuffer supplemented with
2.5 mM EDTA was added, and mitochondria were again
pelleted at 13 000 g for 5 min at 4°C. Then mitochondria
were washed three times with 500 µl of ice-cold MitoBuffer,
resuspended in MitoBuffer containing 0.00625% digitonin
and incubated for 8 min at room temperature to perme-
ate the outer membrane. The resulting mitoplasts were di-
luted with 1 ml of ice-cold MitoBuffer, collected by cen-
trifugation at 13 000 g for 5 min at 4°C, resuspended in
100 µl of MitoBuffer and immediately transferred to 8-well
LabTek or 10-well Grenier slides, pre-treated with poly-L-
lysine. Mitoplasts were fixed for 30 min and then probed

for specific RNAs with custom Alexa Fluor 647-conjugated
branched DNA oligo sets (Supplementary Table S1) using a
ViewRNA ISH Cell assay kit as above but without protein
labelling.

Samples were imaged on an LSM 780 microscope (Carl Zeiss) under a 63×/1.4 oil objective in ProLong Gold Antifade Mountant (Invitrogen). The fluorescent labels were excited at 635 nm (Alexa Fluor 647), 561 nm (mCherry) or 488 nm (Alexa Fluor 488), and their emission was collected (650–750 nm, 570–630 nm, 500–540 nm) with PMT detectors. Image segmentation and quantification of subcellular shapes was done by Squash (73) in a plugin MosaicSuit in Fiji (74). For smFISH analysis of mitoplast-associated RNAs, 7-to-10 frames with a total of 739-to-1477 mitoplasts were quantified.

Statistical analyses

Violin plots were created in Microsoft Power BI Desktop
(v. 2.71.5523.821) with the use of the Epanechnikov kernel
density method. Spearman's correlation was calculated
with Free Statistics and Forecasting Software, v1.2.1
(http://www.wessa.net/rwasp_spearman.wasp). Mann-
Whitney test was performed in Statistics Kingdom (https://
www.statskingdom.com/170median_mann_whitney.html).
One-sample Wilcoxon test and the calculation of con-
fidence intervals (95% CI) for colocalised proportions
of transcripts detected by smFISH were performed in
GraphPad Prism 9 (v9.3.1). The P₀⁻ᵏ simulation in Sup-
plementary Figure S7B was performed in MS Excel 2016
based on the Model 1 and visualised with Heatmapper
(http://heatmapper.ca/). Ensemble RNA folding free energy
was predicted with RNAfold (75). RNA sequences were
retrieved from RefSeq (67).

RESULTS

General principle of CoLoC-seq

In an idealised fractionation experiment, organelles are
isolated and treated with an RNase to degrade contam-
inant RNAs remaining on their surface, leaving resident
transcripts, protected by the organellar membranes, intact.
Those surviving RNAs can then be purified and analysed,
for example, by northern blotting. In reality, due to differ-
ences in sequence, structure and involvement in RNPs, vari-
ous cellular RNAs have unequal sensitivity to RNase treat-
ment, some being digested slowly, if at all. Such transcripts
present a challenging confounding factor for fractionation-
based subcellular transcriptomics since they may be erro-
neously identified as resident, especially when more sen-
sitive amplification-based detection methods, such as RT-
qPCR and RNA-seq, are employed.

To control for such false-positives and rigorously as-
sess the complement of bona fide organelle-resident RNAs.
CoLoC-seq introduces several key innovations in the above
experimental design. The CoLoC-seq pipeline (Figure 1,
top) starts canonically with the isolation of crude or-
ganelles, e.g. by differential centrifugation or affinity purifi-
cation. However, instead of relying on a single snapshot try-
ing to catch a unique RNase treatment condition where all
contaminants would be fully digested (which is practically
unattainable), CoLoC-seq creates a series of reaction snapshots to directly measure the biochemical reactivity of each RNA toward the RNase using classical enzymatic kinetics and thereby estimate the size of the RNA pool that does not take part in the reaction (i.e. remains unavailable to the RNase). To this end, the initial organelle prep is split in a series of identical samples and treated with increasing concentrations of an RNase (including an untreated sample as reference). After a defined time, the reactions are stopped, RNA is isolated and quantitatively analysed by northern blotting or RNA-seq. By assessing the dynamics with which intact transcripts disappear upon the increase of the RNase concentration, one can, by fitting a simple kinetics model (see Model 1 in Materials and Methods), estimate for each detected RNA two parameters: its digestion rate constant (k'), which reflects its sensitivity to RNase, and the proportion of the RNA that is protected from digestion (P₀), which is our parameter of interest. When P₀ = 0, this means that the entire RNA pool participates in the reaction: one observes a gradual disappearance of the transcript in a classical pseudo-first-order decay, indicating that it is a contaminant (Model 2 in Materials and Methods). By contrast, when P₀ >> 0, this indicates that there exists a sizable pool of protected RNA: the curve will plateau at a non-null level, suggesting that this transcript is genuinely present inside the organelle (with P₀ = 1 corresponding to 100% of RNA protected from digestion; Model 3 in Materials and Methods). Thereby, looking at the detailed depletion dynamics of each RNA, one can tell genuinely resident transcripts from even slowly digested contaminants (which may still remain numerically abundant at the highest RNase concentration).

To verify that the protection observed by CoLoC-seq results from the organellar membranes and not from other intrinsic factors (e.g. an intricate tertiary structure or shielding by associated proteins), a Mock CoLoC-seq experiment is performed in parallel (Figure 1, bottom). In this case, a portion of the initial organelle prep is mildly lysed with a detergent under native conditions to dissolve the membranes while minimally perturbing molecular interactions. After this, the RNase treatment and all the subsequent steps are performed in the same way as in a normal CoLoC-seq experiment. In the Mock CoLoC-seq scenario, all transcripts become accessible to the RNase (P₀ = 0) and are expected to show gradual, pseudo-first-order digestion as the RNase concentration increases. If, however, a plateau is still observed (P₀ >> 0), this means that the corresponding RNA is intrinsically resistant to the RNase and may, therefore, represent a false-positive finding. By these means, CoLoC-seq efficiently controls for the two most important sources of false-positive identifications in fractionation-based transcriptomic approaches – slowly digested and RNase-resistant transcripts.

## CoLoC-seq analysis in human mitochondria: proof of principle

We set out to evaluate the feasibility of CoLoC-seq on a relatively well-studied human mitochondrial transcriptome. Mitochondria possess their own genome, providing a straightforward way of benchmarking for bona fide resident transcripts. Indeed, human mitochondria (especially

from the HEK293 cell line used here) are a popular test organelle for subcellular transcriptomics techniques (45–47,49), which permits us to directly compare the performance of CoLoC-seq with that of other existing tools. Of no less interest, a number of reports provided evidence for the presence of select nuclear DNA (nDNA)-encoded transcripts inside these organelles, although this topic remains contentious and, therefore, deserves more rigorous assessment (51,76).

We isolated crude mitochondria from HEK293 cells using a classical protocol, including differential sedimentation and an isopycnic centrifugation step in a sucrose gradient (see Materials and Methods for details). A portion of this mitochondrial prep was lysed to perform a Mock CoLoC-seq experiment. Both lysed and intact mitochondria were divided in 9–10 identical samples and treated with increasing concentrations (from 0 to 3 µg/ml) of RNase A. In 10 min the reactions were stopped, RNA was isolated, spiked-in with a T7 transcript without homology to the human genome (combining parts of a CRISPR guide RNA and a yeast tRNA), and analysed by northern blotting and RNAs-seq (Figure 2). As expected, the mitochondrial DNA (mtDNA)-encoded mt-tRNA<sup>Val</sup> and mt-tRNA<sup>Lys</sup> were fully resistant to RNase A in the CoLoC-seq setup (Figure 2A) but rapidly degraded in Mock CoLoC-seq (Figure 2B), indicating that they are genuinely present inside the organelles. By contrast, abundant nDNA-encoded 5.8S rRNA and U6 snRNA were rapidly degraded in both cases (Figure 2A, B), confirming that they are surface-attached degradable contaminants. Of note, 5S rRNA, which had been proposed to partially localise in mammalian mitochondria (36,57,77–82), plateaued at an intermediate level in both the CoLoC-seq and the Mock CoLoC-seq experiments (Figures 2A, B). This indicates that it remained to a large extent resistant to RNase degradation, even when the mitochondrial membranes had been solubilised, and may, therefore, represent an example of a false-positive identification (see also Supplementary Figure S1). These results show that the CoLoC-seq procedure can qualitatively distinguish between the three main kinds of degradation dynamics produced by topologically different RNA species, as predicted from kinetics principles (Figure 1 and Model 1).

We then subjected the same samples to Illumina sequencing. Since the quantity of interest is the amount of remaining intact RNA, we developed a custom protocol that excludes RNase A-cleaved transcripts (Supplementary Figure S2 and Materials and Methods). To this end, we leveraged the specific chemistry of RNase A cleavage: the enzyme generates 5'-hydroxyl and 3'-phosphate groups, which are incompatible with standard adaptor ligation (63). Thereby only 5'-phosphorylated and 3'-hydroxylated RNAs get amplified, sequenced, and quantified. Importantly, a single RNase A cleavage is sufficient to invalidate the transcript and exclude it from the library.

Upon mapping the reads to the human genome and their cross-library normalisation with the help of an exogenously added spike-in RNA, we could reconstitute the genome-wide digestion dynamics of mitochondria-associated transcripts (Supplementary Table S2). The obtained profiles faithfully recapitulated the behaviour of the select RNA
<figure><img src="image_4.png" /><figcaption>**Figure 2.** CoLoC-seq faithfully recapitulates the depletion dynamics of topologically different mitochondria-associated RNAs. (A, B) Northern blot analysis of a representative CoLoC-seq (A) and the corresponding Mock CoLoC-seq (B) experiments showing how different RNAs react to RNase A in the presence (A) or in the absence (B) of intact mitochondrial membranes. mtDNA-encoded transcripts are highlighted in blue, and nDNA-encoded RNAs are shown in other colours. (C, D) Corresponding CoLoC-seq and Mock CoLoC-seq profiles showing the RNA levels measured by deep sequencing in two compounded replicates (n = 19 for each transcript). Full and broken lines show the fitted kinetics Models 1 and 2, respectively (see also Supplementary Table S2 for values and parameters).</figcaption></figure>

species measured by northern blotting: U6 snRNA and 5.8S rRNA were rapidly degraded to background levels in both CoLoC-seq and Mock CoLoC-seq experiments; 5S rRNA resisted full degradation in both cases; mt-tRNA<sup>Val</sup> remained insensitive to RNase A treatment in CoLoC-seq but quickly disappeared in Mock CoLoC-seq (Figure 2). The overall agreement in RNA proportion measurements between the conventional technique (northern blotting) and CoLoC-seq was very good (Spearman's ρ = 0.888, *P* = 8.7 × 10<sup>-73</sup>), with 90.6% of proportion measurements being within 0.3 from each other (Supplementary Figure S5A). As a more stringent test, we fitted the kinetics model into the data quantified by northern blotting or by CoLoC-seq and found that the resulting *P*<sub>0</sub> values were in general highly concordant (Spearman's ρ = 0.822, *P* = 1.6 × 10<sup>-5</sup>) and did not show signs of significant bias (Supplementary Figure S5B). These results indicate that CoLoC-seq correctly and quantitatively captures the kinetic behaviour of analysed transcripts, evaluates the size of the protected RNA pool,

and thereby distinguishes between resident RNAs, degradable and non-degradable contaminants.

## CoLoC-seq analysis of the entire human mitochondrial transcriptome

We extended our CoLoC-seq pipeline to interrogate the genome-wide topology of mitochondria-associated RNAs relative to the mitochondrial membranes. We expectedly observed that in CoLoC-seq libraries the proportion of mtDNA-derived reads increases as the RNase A concentration grows. This corresponds to the selective protection of mitochondria-localised transcripts against the backdrop of the gradual bulk degradation of surface-exposed RNAs (Figures 3A). This enrichment, however, did not occur in Mock CoLoC-seq libraries, indicating a similar susceptibility of mtDNA- and nDNA-encoded transcripts to degradation once the mitochondrial membranes are gone (Figure 3B).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** RNA localisation topology landscape of mitochondria-associated transcripts. (A, B) The evolution of library composition across the gradient of RNase A concentrations in CoLoC-seq (A) and Mock CoLoC-seq (B). Only in CoLoC-seq, mtDNA-encoded transcripts get progressively enriched, while most nDNA-encoded RNA classes are depleted. (C) 350 mitochondria-associated transcripts are plotted according to their protected proportions P₀ measured in CoLoC-seq and Mock CoLoC-seq. The probability of mitochondrial residency increases from left to right; the intrinsic resistance of transcripts to RNase A increases from bottom to top. See also Supplementary Table S2 for the underlying data and Figure 4A for a more detailed view of the lower right quadrant, where candidate resident RNAs are located.</figcaption></figure>
Using a stringent read count cut-off to enable robust profiling and model fitting (see Materials and Methods), we identified 351 non-redundant transcripts as significantly associated with mitochondria, including 35 mtDNA-encoded ones, which corresponds to 13,338 individual RNA level measurements (Supplementary Table S2). The CoLoC-seq replicates were well-correlated with each other (Spearman's $ρ = 0.774 ± 0.036$, mean ± SD for RNase A concentration-matched samples); so were the Mock CoLoC-seq replicates (Spearman's $ρ = 0.700 ± 0.112$, Supplementary Figure S6A). By contrast, the CoLoC-seq samples were markedly different from the Mock CoLoC-seq ones (Spearman's $ρ = 0.383 ± 0.104$; two-tailed Mann-Whitney test $P = 1.7 × 10^{-6}$), indicating globally dissimilar digestion patterns (Supplementary Figure S6A). Furthermore, within each CoLoC-seq or Mock CoLoC-seq dataset, samples treated with different RNase A concentrations also tended to correlate with each other, whereas there was little cross-correlation between CoLoC-seq and Mock CoLoC-seq samples (two-tailed Mann-Whitney test $P = 4.4 × 10^{-16}$, Supplementary Figure S6A). This suggests that the ensemble of interrogated RNAs followed consistent reaction paths along the entire range of RNase A concentrations, and these dynamics differed between CoLoC-seq and Mock CoLoC-seq, as could already be inferred from library compositions (Figure 3A, B).

We then automatically fitted the CoLoC-seq kinetics models into these data to estimate the key parameters of the corresponding reaction curves: the protected proportion $P_0$ and the effective digestion rate constant $k'$ (see Materials and Methods). The biological replicates returned highly concordant $P_0$ values (Spearman's $ρ = 0.815 ± 0.042$, mean ± range), whereas CoLoC-seq and Mock CoLoC-seq samples were more dissimilar (Spearman's $ρ = 0.47 ± 0.14$, Supplementary Figure S6B). This remaining correlation is not surprising, given that the majority of mitochondria-associated RNAs behave as contaminants and follow similar digestion dynamics with $P_0$ values close to 0 in both CoLoC-seq and Mock CoLoC-seq setups (see below).

Due to the high similarity of replicates, we could merge them and thereby increase statistical power and the accuracy of kinetics estimates. Unsupervised fitting of the complete Model 1 was successful in 679 cases (96.7% out of 351 CoLoC-seq and 351 Mock CoLoC-seq profiles). An additional 22 profiles, for which $P_0$ and $k'$ were too strongly intertwined (suggesting that in their case the Model 1 was unnecessarily complex), could be rescued with the simpler Model 2 (see Materials and Methods), bringing the overall fitting success rate to 99.9%. The latter was in particular the case for many mtDNA-encoded RNAs in CoLoC-seq experiments, where their apparent digestion rate approached 0 (due to protection by mitochondrial membranes) and the profile remained largely flat ($R^2 = -0.12 ± 0.22$, mean ± SD, Supplementary Table S2).

We wondered whether the digestion rate constants we obtained by fitting the kinetics models were biochemically meaningful. The vast majority of rate constants measured in our CoLoC-seq and Mock CoLoC-seq experiments were fairly close to $k_{cat}/K_m$ reported for a standard RNase A substrate, UpA ($2.3 × 10^6 M^{-1} s^{-1}$, Supplementary Figure S7A) (83). Only in rare cases, e.g. for 5S rRNA ($k = 1.9 × 10^4 M^{-1} s^{-1}$), was the apparent digestion rate worse than that of a poor RNase A substrate, UpOC₆H₄-p-NO₂ ($k_{cat}/K_m = 5.7 × 10^4 M^{-1} s^{-1}$) (83), confirming its remarkable recalcitrance to digestion (Figure 2).

We then tested whether the observed rate constants permitted independent estimation of the size of protected RNA pools $P_0$. By simulating data for a continuum of $k-P_0$ combinations, we observed that the CoLoC-seq Model 1 remains robust at $k > 10^5 M^{-1} s^{-1}$ but becomes uncertain when digestion occurs too slowly (Supplementary Figure S7B). Nearly all experimental $k$ values measured by CoLoC-seq and Mock CoLoC-seq fell within the optimal dynamic range, enabling unequivocal, untangled $P_0$ estimation. We found that $P_0$ estimates were not appreciably influenced by RNA abundance, pyrimidine content, or overall amount of structure (Supplementary Figure S7C-E). Altogether, these results indicate that CoLoC-seq is a reproducible approach, capturing biochemically meaningful parameters from a myriad of simultaneously going RNase digestions, that can be used for a reliable and unbiased estimation of accessible and inaccessible RNA pools.

To globally visualise the localisation topology of mitochondria-associated transcripts we plotted the protected RNA pools $P_0$, as measured in CoLoC-seq versus Mock CoLoC-seq (Figure 3C). Thereby we could roughly partition all transcripts in three unequal groups. The majority of RNAs (~77%), had low $P_0$ values (<0.1) in both CoLoC-seq and Mock CoLoC-seq experiments, which identifies them as trivial degradable contaminants. This group includes all long nDNA-encoded transcripts (mRNAs, lncRNAs), 5.8S rRNA, 18S rRNA, 28S rRNA, snRNAs, snoRNAs and scaRNAs. The absence of long nDNA-encoded RNAs inside human mitochondria has also been observed by proximity labelling approaches, corroborating our observation (45–47,49). Another, small, group of RNAs on the opposite extreme of the diagonal showed relatively high $P_0$ values in both CoLoC-seq and Mock CoLoC-seq, meaning that they are at least partially intrinsically resistant to RNase A, independently of the integrity of the mitochondrial membranes (Figure 3C). Strikingly, this group contains three small noncoding RNAs (ncRNAs), which are most frequently cited as potentially imported into the mammalian mitochondria: 5S rRNA, RMRP (RNA component of RNase MRP) and RPPH1 (RNA component of the nuclear RNase P) (36,51,57,77–82,84–86). Their localisation in this quadrant suggests that they may in fact be RNase-resistant false positives. Indeed, all these RNAs are highly structured and embedded in very proteinaceous house-keeping RNPs (87–89). Therefore, it is not possible to conclude about the localization of this group of transcripts solely based on their resistance to RNase, and orthogonal methods are required to draw the line (see below).

The most interesting part of this landscape, harbouring ~18% of all mitochondria-associated RNAs, featured high $P_0$ values in CoLoC-seq, where mitochondria were intact, but low $P_0$ values in Mock CoLoC-seq, where they were lysed (Figures 3C, 4). This suggests that they are specifically protected by mitochondrial membranes and, therefore, likely represent bona fide resident transcripts. These RNAs include 34 mtDNA-encoded transcripts. Only one
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Human mitochondria-resident transcripts identified by CoLoC-seq. (A) Detailed view of the high CoLoC-seq *P*₀ – low Mock CoLoC-seq *P*₀ area from Figure 3C showing mtDNA-encoded (blue) and nDNA-encoded (other colours) transcripts specifically protected from degradation by mitochondrial membranes. (B, C) Corresponding CoLoC-seq (B) and Mock CoLoC-seq (C) profiles with fitted kinetics models indicate that these RNAs significantly resist RNase A-mediated depletion when the mitochondrial membranes are intact but get rapidly degraded once they are solubilised. Mitochondrial 16S rRNA (RNR2) is shown as an unusually RNase-resistant mtDNA-encoded transcript (see also Figure 3C and Supplementary Table S2 for the underlying data).</figcaption></figure>
mtDNA-encoded RNA, 16S rRNA/RNR2, showed an un-
usually high resistance to RNase A, likely because it is em-
bedded in the extensively proteinaceous large mitoriboso-
mal subunit (90). Unlike proximity labelling approaches,
which successfully identified long polyadenylated mtDNA-
encoded RNAs but could not efficiently handle small RNA
species (45–47,49), CoLoC-seq also captured 15 out of 22
mitochondrial tRNAs. This highlights its ability to inter-
rogate this traditionally more challenging part of the tran-
scriptome.

Strikingly, a distinct group of small PolIII-transcribed ncRNAs occupied a similar position in the plot, suggesting their mitochondrial localisation (Figure 3C and 4A). Those include the highly conserved Y1 and Y3 RNAs (91,92), the primate-specific SNAR-A RNAs (93), and a set of tRNAs. Manual inspection of their profiles confirmed that they had a significant RNase A-resistant pool in CoLoC-seq, when mitochondria were intact, but not in Mock CoLoC-seq, when they were lysed (Figure 4B, C, Supplementary Table S2). As far as the read length (75 nt) permits to see, all these RNAs appear to be full-size, untruncated transcripts (Supplementary Figure S8A–C). The extensive association of cytosolic tRNAs with human mitochondria has been previously reported (36), while the functional significance of this phenomenon remains enigmatic. The composition of the detected tRNAs is strongly biased: Glu, Asp, Lys and Gly isoacceptors overwhelmingly dominate the landscape (Supplementary Figure S8D). These tRNAs are neither the most abundant nor the least charged in HEK293 cells (94–96). However, these tRNA families are exactly those containing the smallest number of Watson-Crick face-disrupting methylations and consecutive dihydroouridines, which enables their preferential, if not exclusive, coverage in standard RNA-seq experiments (96–98). Indeed, the recent mim-tRNAseq analysis of human tRNAs (96) indicates that these tRNA families have the lowest number of modified residues causing TGIRT-mediated nucleotide misincorporation (Supplementary Figure S8E). More traditional reverse transcriptases used in RNA-seq library construction hard-stop on such positions, strongly biasing the apparent tRNA diversity. With this consideration in mind, we do not find any obvious specificity in tRNA association with mitochondria.

Nucleus-encoded mitochondria-localised RNAs are primarily stuck in the intermembrane space

Mitochondria have two membranes that delimit the in-
nermost matrix subcompartment and the intermembrane
space (IMS). We wondered where exactly in mitochondria
the newly identified nDNA-encoded RNAs could be lo-
cated. This question is important as their eventual pres-
ence in the matrix would mean that they could function-
ally interact with the mitochondrial genetic system. To this
end, we subjected crude HEK293 mitochondria to a stan-
dard submitochondrial fractionation procedure frequently
employed to interrogate protein localisation (see Materials
and Methods). Intact mitochondria, mitoplasts (mitochon-
dria with an osmotically ruptured outer membrane), or
fully lysed mitochondria were treated or not with an RNase
A/T₁ mix; the remaining RNA was extracted and analysed

by northern blotting. As shown in Figure 5A, mtDNA-
encoded RNAs were resistant to RNase A/T₁ digestion in
both mitochondria and mitoplasts, indicating their matrix
localisation. By contrast, U6 snRNA was degraded in all
RNase A/T₁-treated samples, corresponding to its extrami-
tochondrial localisation. Interestingly, both Y RNAs and
the cytosolic tRNAs we probed for remained fairly resistant
to RNase A/T₁ in intact mitochondria but were strongly
depleted upon the disruption of the outer membrane. This
suggests that the majority of these molecules localise to the
intermembrane space, rather than to the matrix.

As an independent, *in situ* approach, we interrogated
the subcellular localisation of Y RNAs using smFISH in
HepG2 cells (Figure 5B). Y1 and Y3 RNAs showed both
cytoplasmic and nuclear localisation in HepG2 cells, as pre-
viously reported (99,100), and accumulated to several dozen
copies/cell. They were frequently found next to mitochon-
dria and on some rare occasions (3–5 per cell) clearly colo-
calised with the organelles. The RNase A-resistant RMRP,
which has established functions in both the nucleus and the
cytoplasm (88,101–103), showed a similar localisation pat-
tern (Supplementary Figure S9A). The mtDNA-encoded
MT-CO1 mRNA showed a nearly complete colocalisation
with the mitochondrial marker (Figure 5B), whereas 5S
rRNA and 5.8S rRNA were far too abundant to conclude
about their degree of colocalisation (Supplementary Figure
S9A), as previously reported (51).

Given the resolution limit of confocal microscopy, the
observed mitochondria-associated RNAs could be either
internally localised or merely attached to the surface of
the organelles. To distinguish between these two possibil-
ities, we performed smFISH on isolated mitoplasts from
a HEK293 cell line expressing a matrix-localised PDHA1-
mCherry marker. This cell model allows for a facile track-
ing of mitochondria, distinguishing them from other copu-
rified organelles and cell debris. Additionally, the disruption
of the outer membrane significantly enlarges mitoplasts, fa-
cilitating the observation of eventual colocalisation events
(Supplementary Figure S9B). Importantly, the highly spe-
cific branched DNA smFISH technology, relying on joint
co-binding of at least two probes and *in situ* signal ampli-
fication, permits the detection of single transcripts, inde-
pendently of their length (71,72). Figure 5C, D and Sup-
plementary Figure S9C show that the mtDNA-encoded
MT-CO1 and mt-tRNA<sup>Val</sup> were predominantly colocalised
with mitoplasts, as expected (1258/1493, or 84.3%, of de-
tected MT-CO1 molecules, 95% CI from 82.3% to 86%).
By contrast, nearly all detected 5S rRNA and 5.8S rRNA
molecules lay outside the mitoplasts. The same was true for
the less-abundant RMRP: the few spots observed in the
mitoplast preparations hardly ever colocalised with mito-
plasts (1/57 occurences, 95% CI from 0% to 10.2%, Sup-
plementary Figure S9C). Interestingly, both Y1 and Y3
RNAs could be detected either as stand-alone particles
or as mitoplast-localised transcripts. However, the num-
ber of such events was extremely small, especially in com-
parison with mtDNA-encoded RNAs: only 10 Y1 RNA
molecules were observed per 739 examined mitoplasts, 2
of which colocalised with mitoplasts (95% CI from 4.6%
to 52.1%); and out of 5 detected Y3 RNA molecules (per
935 mitoplasts), 2 showed colocalisation (95% CI from
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Nuclear DNA-encoded mitochondrial RNAs are scarce in the mitochondrial matrix. (A) Submitochondrial fractionation of mitochondria-associated transcripts. Intact mitochondria, mitoplasts obtained by osmotic disruption of the outer membrane (MP), or lysed mitochondria from HEK293 cells were treated with an RNase A/T₁ mixture ('1×' corresponds to 1 µg of RNase A and 2.5 U of RNase T₁ per mg of mitochondrial protein) or left untreated. Surviving RNAs were analysed by northern blotting with probes to select mtDNA- (*MT-ND3*, mt-tRNA<sup>Val</sup>) and nDNA-encoded (Y1 and Y3 RNAs, tRNA<sup>Glu</sup><sub>CTC</sub>, tRNA<sup>Asp</sup><sub>GTC</sub>, U6 snRNA) transcripts. (B) The mtDNA-encoded *MT-CO1* mRNA and two nDNA-encoded transcripts (Y1 and Y3 RNA) were detected in HepG2 cells by branched DNA smFISH and visualised under a confocal microscope (green). TOMM20, an outer mitochondrial membrane protein, was visualised by immunofluorescence (magenta). Size bar is 10 µm. Zoom-in views (10 × 10 µm) are provided for select cases of apparent colocalisation (white). See also Supplementary Figure S9A. (C) Mitoplasts of HEK293 cells expressing PDHA1-mCherry were prepared from crude RNase A-treated mitochondria by digitonin-mediated disruption of the outer membrane and subjected to smFISH as in panel (B). Size bar is 10 µm. Zoom-in views (10 × 10 µm) are provided for select cases of colocalisation (white, shown with arrows for Y RNAs) or of absence thereof. See also Supplementary Figure S9B, C. (D) Quantification of the data shown in (C). For each analysed transcript, the proportion of RNA spots colocalising with mitoplasts (±95% CI) is plotted versus the number of mitoplast-localised RNA spots per 1000 examined mitoplasts. Note that for some particularly abundant transcripts the 95% CI bars are very small.</figcaption></figure>
11.6% to 77.1%). Given their scarcity, assuming a Poisson distribution, these spots likely correspond to single Y RNAs. Thereby, in comparison with the mtDNA-encoded *MT-CO1* mRNA, this effectively sets the higher limit of their abundance at <1 per 300 mtDNA-encoded transcripts (taking the hyper-conservative approach assigning one mtDNA-encoded RNA to one observed mitoplast; a more realistic estimate, accounting for its higher real abundance and the fact that the *MT-CO1* mRNA is but one of many mtDNA-encoded transcripts, is likely 3-4 orders of magnitude lower). Altogether, our data suggest that, while some nDNA-encoded RNAs seem to be present in human mitochondria, their abundance in the matrix is extremely low.

## Y RNA-associated Ro60 protein is found in mitochondria but does not interact with PNPase

Finding Y RNAs in mitochondria was unexpected and made us wonder whether their key protein partner Ro60, which is required for their stability (91,104), may similarly be found in this compartment. Because Ro60 is abundant (92), it is difficult to judge about its possible association with the mitochondria by immunofluorescence (Supplementary Figure S10A). Therefore, we performed the same submitochondrial fractionation analysis, as in the case of RNAs (Figure 5A), but using resistance to proteinase K digestion as a probe for the differential submitochondrial localisation of proteins (Figure 6A and Supplementary Figure S10B). The known mitochondrial matrix-localised proteins LRPPRC and the mitoribosomal protein uL4m remained resistant to proteinase K in all samples but the lysed one. The inner membrane-embedded, IMS-exposed OPA1 protein was protected in intact mitochondria but digested upon the outer membrane rupture. The cytosol-exposed TOMM20 protein was sensitive to proteinase K in all samples (105). Strikingly, Ro60, independently identified with two different antibodies, behaved similarly to LRPPRC and uL4m (Figure 6A and Supplementary Figure S10B). Moreover, an α-Ro60 antibody coimmunoprecipitated several mitochondrial proteins from total HEK293 cell lysates (Supplementary Figure S10C). This suggests that it may indeed be present within the interior space of mitochondria along with its Y RNA ligands.

Ro60 and Y RNAs were found to form a stable RYPER complex with PNPase, which helps to degrade structured RNA molecules in *Deinococcus radiodurans* and *Salmonella enterica* (106). Since the human PNPase orthologue has been reported to localise in the mitochondrial IMS and matrix (107,108) (Supplementary Figure S10B), we wondered whether a similar assembly may exist in human cells. To this end, we immunoprecipitated Ro60 from crude HEK293 mitochondria and found that it stably interacted with both Y RNAs but not with PNPase (Figure 6B), ruling out the formation of a stable RYPER-like complex in humans.

## DISCUSSION

Fractionation-based techniques remain the most accessible and widely used subcellular transcriptomics approaches due to their simplicity, the easy handling of organelles even

<figure><img src="image_8.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_9.png" /></figure>

**Figure 6.** Ro60 is likely present in human mitochondria. (A) Submitochondrial fractionation of mitochondria-associated proteins. Intact mitochondria, mitoplasts (MP), or lysed mitochondria, obtained from HEK293 cells as in Fig. 5A, were treated with proteinase K or left untreated. Surviving proteins were analysed by western blotting. LRPPRC and uL4m are matrix proteins. OPA1 is an inner membrane protein exposed into the IMS. TOMM20 is an outer membrane protein facing the cytosol. Ro60 was detected with a mouse monoclonal antibody (Santa Cruz Biotechnology, sc-100844). See also Supplementary Figure S10B for a replicate experiment using a different anti-Ro60 antibody. (B) Ro60 was immunoprecipitated from crude HEK293 mitochondria, and the coimmunoprecipitates were analysed by western blotting (Ro60, PNPase) or northern blotting. ‘Lys’ – mitochondrial lysate, ‘FT’ – flow-through. Note that the Ro60 level in the lysate was below the detection limit; it could only be seen in the strongly enriched specific coIP fraction.
from genetically intractable, non-model organisms and tissues, and a high amount of material, permitting to obviate rRNA depletion and poly(A) enrichment and profile potentially all RNA classes. At the same time, their false-positive calling rate remains prohibitively high to reliably judge about the topology of RNA localisation. Here we presented a more sophisticated, topology-aware, and kinetically controlled CoLoC-seq method that corrects for false-positive identifications due to slow degradation and flags intrinsically RNase-resistant transcripts, which may be potentially false-positive. The general reaction model used by CoLoC-seq permits to robustly identify the transcripts residing inside membrane-bounded organelles, such as mitochondria. This question of localisation topology is of high importance for our understanding of how complex genetic systems operate and, in the case of the mitochondria, to what extent nDNA-encoded RNAs may intervene into post-transcriptional processes in the organelles.

By identifying nearly all mtDNA-encoded long coding and noncoding RNAs as resident, CoLoC-seq compares favourably with most advanced proximity labelling approaches, such as APEX-RIP, APEX-seq and CAP-seq, previously applied to the same model (45-47,49). Moreover, similarly to these techniques, CoLoC-seq did not find evidence for mitochondrial localisation of nDNA-encoded mRNAs or lncRNAs in HEK293 cells. By contrast, CoLoC-seq significantly outperforms the proximity labelling approaches in handling relatively short transcripts: it confidently identified 15 out of 22 mt-tRNAs (for comparison, the best performance, shown by APEX-RIP, was only 3 mt-tRNAs - (45)). Currently, only two proximity labelling methods, Halo-seq (109) and uridylation-mediated RNA recording (50), have achieved a similar degree of sensitivity for shorter RNAs (such as tRNAs). However, due to limitations imposed by its chemistry, Halo-seq cannot deal with mitochondria-localised transcripts, which are naturally oxidised due to their proximity to the electron-transport chain, while the performance of the RNA recording has yet to be tested in the mitochondrial interior.

The unique ability of CoLoC-seq to interrogate, in an unbiased way, shorter RNAs enabled us to address a long-standing and controversial question of the localisation topology of nDNA-encoded transcripts in mammalian mitochondria (51,76). For instance, although a few spliceosomal components had been detected in mitochondrial preparations (110), CoLoC-seq clearly classified them as surface-attached, even though highly abundant, contaminants (Figure 3), ruling out the existence of a splicing machinery in these organelles. A trickier and thornier case is the much-debated presence in human mitochondria of 5S rRNA, RMRP and RPPH1 RNAs (51,76). By comparing the behaviour of these transcripts in CoLoC-seq and Mock CoLoC-seq, we found that they were remarkably recalcitrant to RNase A degradation even when there were no membranes to protect them (Figures 2, 3 and Supplementary Figure S1). Direct microscopy observation of 5S rRNA and RMRP in preparations of RNase-treated mitoplasts confirmed that these RNAs were present nearly exclusively in extra-mitochondrial material (Figure 5C), suggesting that they were protected from degradation by other means, e.g. associated RNA-binding proteins. This finding cautions against such false-positive cases, which, however rare, cannot be easily spotted in simpler fractionation-based experimental setups and require cross-validation by orthogonal approaches. Of note, the extensive protection of RNAs by proteins (111–113) or by intricate structure (114) has been recognised as a major challenge in the extracellular RNA field, which more recently urged the community to reconsider the localisation of exported RNAs within free ribonucleoproteins (RNPs) versus extracellular vesicles (16,17,38,40–42,115–119).

We detected cytosolic tRNAs, SNAR-A and Y RNAs as significantly mitochondria-associated. With their primary localisation to the IMS, the functional significance of their mitochondrial destination is unclear. SNAR-A are poorly characterised primate-specific PolIII transcripts, normally found in the cytoplasm in association with cytosolic ribosomes (93). Y RNAs, stably associated with the deeply conserved Ro60 protein, are much better studied and form RNPs involved in RNA quality control and turnover (91). Given that they are present in 5% bacteria, where they team up with proteins like PNPase to better degrade structured RNAs (106,120), their finding in the mitochondria probably should not appear so surprising. However, despite their apparent colocation (in the IMS and/or the matrix), we do not find evidence for a stable Y RNA-Ro60-PNPase complex in human cells. More importantly, although the detection of Y RNAs inside isolated RNase-treated mitoplasts supports their low-level presence in the matrix, their estimated copy number in this compartment is extremely low. The mitochondrial and nuclear RNA worlds thus appear largely separated in human cells.

CoLoC-seq has been designed based on basic biochemical principles and must in principle be applicable to any membrane-bounded or, more specifically, RNase-impermeable entities. Among them, genome-containing organelles, such as chloroplasts and apicoplasts, but also organelle-like endosymbionts and essentially all enveloped and even some non-enveloped viruses, are all objects of highest interest, as their ability to import extraneous RNA, which may have important functional and evolutionary implications, has not been systematically evaluated. Similarly, robust profiling of the RNA content of extracellular vesicles from both eukaryotic and bacterial sources remains problematic in terms of localisation topology and may require an adapted CoLoC-seq pipeline, e.g. incorporating a proteinase K pre-treatment step to expose RNP-embedded contaminants (112,115–117).

## Limitations of CoLoC-seq

As for now, CoLoC-seq did not explicitly profile very short transcripts, such as miRNAs. Therefore, their possible localisation inside the human mitochondria remains unanswered. It will likely require a number of significant technical tweaks which are beyond the scope of this paper. A major hurdle in this direction is the nearly complete shielding of miRNAs by the associated Ago proteins (112) which, compounded by a generally lower susceptibility of miRNAs to degradation (62), will yet have to be dealt with by essentially all subcellular transcriptomics approaches. In a similar vein, the observed tRNA diversity was likely affected by the extensive modification of many cytosolic and some mtDNA-encoded tRNAs, which can be
proved with existing demethylating and TGIRT-dependent tRNA-seq strategies (96,97,121,122). Due to the specific sequencing protocol, CoLoC-seq is naturally blind to regular 5'-OH and 2'/3'-phosphorylated RNA species (e.g. tRNA fragments) and to circRNAs. However, those can still be visualised and quantified by northern blotting. Finally, one may expect that CoLoC-seq might have missed some low-abundance RNAs that could still be localised in the mitochondria. However, the copy number estimates for mitochondria-localised Y RNAs enabled by smFISH in isolated mitoplasts (Figure 5C) suggest that we have reached the limit of biologically relevant detection, and the existence of further such transcripts is unlikely.

## DATA AVAILABILITY

Scripts used for alignment, coverage calculation, and feature quantification of CoLoC-seq data can be found at Zenodo (https://doi.org/10.5281/zenodo.6389451). The CoLoC-seq and Mock-CoLoC-seq data presented in this article have been deposited in NCBI Gene Expression Omnibus (GEO) under accession number GSE183167 [link to access the dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183167]. The mass spectrometry proteomics data have been deposited in open access to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037383.

## SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

## ACKNOWLEDGEMENTS

The authors wish to thank Dr Fritz Thümmler (Vertis Biotechnologie AG) for the customisation of the CoLoC-seq library pipeline, Timothée Vincent for help with pilot CoLoC-seq data analyses, Lauriane Kuhn (Proteomics Platform Esplanade, Strasbourg) for mass spectrometry analyses, and all members of the MITO team, UMR7156 - GMGM, and the LabEx MitoCross for stimulating and fruitful discussions.

## FUNDING

University of Strasbourg through the IdEx Unistra [ANR-10-IDEX-0002]; SFRI-STRAT_US project [ANR-20-SFRI-0012]; EUR IMCBio in the frame of the Research Cluster MitoCross [ANR-17-EURE-0023]; IdEx - Attraction [ANR-10-IDEX-0002-02 to Al.S.]; Centre National de la Recherche Scientifique (CNRS). Funding for open access charge: IdEx Unistra [ANR-10-IDEX-0002]. *Conflict of interest statement.* None declared.

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