molb-07-00209 Augus 13, 2020 Time: 17:7 # 1
PERSPECTIVE
published: 14 Augus 2020
doi: 10.3389/ molb.2020.00209
Edi ed by:
Eleono a Leucci,
KU Leu en, Belgium
Re iewed by:
Sil ia Bo ini,
Uni e si é Cô e d’Azu , F ance
S ephen J. Bush,
Uni e si y o Ox o d, Uni ed Kingdom
Da id Hume,
The Uni e si y o Queensland,
Aus alia
Pa ik S åhl,
KTH Royal Ins i u e o Technology,
Sweden
*Co espondence:
Anca F. Sa ulescu
[email p o ec ed];
[email p o ec ed]
Special y sec ion:
This a icle was submi ed o
P o ein and RNA Ne wo ks,
a sec ion o he jou nal
F on ie s in Molecula Biosciences
Recei ed: 19 Feb ua y 2020
Accep ed: 30 July 2020
Published: 14 Augus 2020
Ci a ion:
Sa ulescu AF, Jacobs C,
Negishi Y, Da ignon L and
Mhlanga MM (2020) Pinpoin ing Cell
Iden i y in Time and Space.
F on . Mol. Biosci. 7:209.
doi: 10.3389/ molb.2020.00209
Pinpoin ing Cell Iden i y in Time and
Space
Anca F. Sa ulescu1*, Ca on Jacobs1,2,3, Yu aka Negishi1, Lau ianne Da ignon1and
Musa M. Mhlanga1,3,4
1Di ision o Chemical, Sys ems & Syn he ic Biology, Facul y o Heal h Sciences, Ins i u e o In ec ious Disease & Molecula
Medicine, Uni e si y o Cape Town, Cape Town, Sou h A ica, 2SAMRC/NHLS/UCT Molecula Mycobac e iology Resea ch
Uni , Depa men o Pa hology, Ins i u e o In ec ious Disease and Molecula Medicine, Uni e si y o Cape Town, Cape Town,
Sou h A ica, 3Wellcome Cen e o In ec ious Diseases Resea ch in A ica, Uni e si y o Cape Town, Cape Town,
Sou h A ica, 4Ins i u o de Medicina Molecula , Faculdade de Medicina da Uni e sidade de Lisboa, Lisbon, Po ugal
Mammalian cells display a b oad spec um o pheno ypes, mo phologies, and unc ional
niches wi hin biological sys ems. Ou unde s anding o mechanisms a he indi idual
cellula le el, and how cells unc ion in conce o o m issues, o gans and sys ems,
has been g ea ly acili a ed by cen u ies o ex ensi e wo k o classi y and cha ac e ize
cell ypes. Classic his ological app oaches a e now complemen ed wi h ad anced
single-cell sequencing and spa ial ansc ip omics o cell iden i y s udies. Eme ging
da a sugges s ha addi ional le els o in o ma ion should be conside ed, including
he subcellula spa ial dis ibu ion o molecules such as RNA and p o ein, when
classi ying cells. In his Pe spec i e piece we desc ibe he impo ance o in eg a ing
cell ansc ip ional s a e wi h issue and subcellula spa ial and empo al in o ma ion o
ho ough cha ac e iza ion o cell ype and s a e. We e e o ecen s udies making use
o single cell RNA-seq and/o image-based cell cha ac e iza ion, which highligh a need
o such in-dep h cha ac e iza ion o cell popula ions. We also desc ibe he ad ances
equi ed in expe imen al, imaging and analy ical me hods o add ess hese ques ions.
This Pe spec i e concludes by aming his a gumen in he con ex o p ojec s such as
he Human Cell A las, and ela ed ields o cance esea ch and de elopmen al biology.
Keywo ds: spa io empo al localiza ion, cell sub ype classi ica ion, spa ial ansc ip omics, MRNA subcellula
localiza ion, cell sub ype
INTRODUCTION
Biology inhe en ly equi es classi ica ion o manage as amoun s o i educibly complex
in o ma ion. Biological sys ems a e b oken up in o o gans, issues, cells and molecula pa hways,
whe e cells make up he smalles unc ional uni s o li e. Cells mus hus occupy a wide ange o
pheno ypes and mo phologies, ul illing he unc ional equi emen s o each o hei issues and
niches. Ou unde s anding o his di e si y is acili a ed by classi ying cells ac oss his spec um as
di e en cell “ ypes” ca ying speci ic molecula signa u es. Howe e , cells also exis in dynamic
s a es wi h some unc ional plas ici y, which p esen s pa icula challenges o a educ ionis
classi ica ion app oach.
The e a e wo ypes o classi ica ion e o s we a e a isk o making: ei he assigning he same
iden i y o wo cells when hey a e di e en , o con e sely, labeling wo cells as di e en when hey
a e unc ionally iden ical. To a oid hese e o s, we need o be e unde s and he pa ame e s ha
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dis inguish cells, including he ela ionship be ween cell s a e,
unc ion, and iden i y, o be able o delinea e cell sub- ypes
and classes wi h g ea e esolu ion. To illus a e ou a gumen ,
conside a hypo he ical si ua ion: wo cells a e adjacen o one
ano he in a issue sample and possess simila le els o he same
RNA ansc ip s. Does his conclude ha hey a e he same
cell ype? In e sely, i hey ha e di e ing le els o ansc ip s,
does his mean hey a e di e en , o could hey simply be
in di e en s a es o s ages in a p ocess? Cells exis in lux,
ac oss con inuous spec a o di e en ia ion and s a e. They
p og ess h ough i e e sible p ocesses, such as de elopmen
o di e en ia ion; oscilla o y p ocesses such as he cell cycle
and ci cadian hy hms; as well as e e sible ansi ions be ween
s a es, including nu i ional o disease s a us. These changes
na u ally mani es in a cell’s beha io , molecula composi ion and
subcellula o ganiza ion o a ious componen s. I is in ui i e
o unde s and p ocesses such as de elopmen o di e en ia ion
as p og essions o cell ype o iden i y. Howe e , i is less clea
i cells in dis inc bu ansi o y s a es should be assigned o
dis inc sub-ca ego ies.
As a co olla y, by using cu en echniques ha p ima ily
conside cells’ molecula composi ion (and e en hei
o ganiza ion wi hin a issue) we canno clea ly de e mine
whe e in a p ocess a gi en cell may be, and hus how simila o
dis inc one cell iden i y may be om ano he .
SINGLE-CELL GENOMICS AS THE
STANDARD APPROACH TO IDENTIFY
CELL TYPES AND STATES
A cell’s iden i y is de e mined by i s lineage, p esen s a e,
and u u e di e en ia ion o unc ional po en ial, as well as i s
spa ial con ex wi hin a issue o sys em (Wagne e al., 2016).
Based on he unde s anding ha his iden i y is e lec ed in
he molecula composi ion o he cell, single-cell genomics and
p o eomics ha e become s anda d app oaches o cha ac e ize
cell iden i y and s a e. Single-cell genomics includes measu ing
gene exp ession, ypically ia RNA sequencing (RNA-seq), as well
as epigene ic s a es and ch oma in s uc u e, using app oaches
which ha e been adap ed o wo k a he single-cell le el ( e iewed
in T apnell, 2015;Ludwig and Bin u, 2019;Shema e al., 2019).
Single-cell RNA-seq (scRNA-seq) has been apidly de eloped and
is he mos popula app oach o iden i y cell ypes as i enables
classi ica ion o cells unbiasedly, based on gene exp ession
pa e ns and allows o iden i y no el cell ypes and sub ypes
wi hou p io knowledge ( o example Da manis e al., 2015;
G ün e al., 2015;Shekha e al., 2016;Villani e al., 2017;
Sch oede e al., 2020). Typical cell- ype iden i ica ion by scRNA-
Seq in ol es dissocia ing single cells om issues, ollowed
by isola ion o hei RNA, e e se ansc ip ion, ampli ica ion,
sequencing and compu a ional analysis (Figu e 1A). Howe e ,
he e a e se e al limi a ions o scRNA-Seq me hods: (1) ade-
o be ween he numbe o cells and da a quali y; (2) limi ed
measu emen o p o ein exp ession; (3) noise le el and; (4) lack o
spa ial and empo al in o ma ion. As echnical de elopmen s in
scRNA-Seq me hodology, including compu a ional me hods a e
well documen ed in o he e iews (Hwang e al., 2018;Chen G.
e al., 2019;Liao e al., 2020), we discuss ep esen a i e scRNA-
Seq me hods and desc ibe hei ad an ages and limi a ions below.
TRADE-OFF BETWEEN NUMBER OF
THE CELLS AND DATA QUALITY
Cu en scRNA-Seq echniques can be classi ied based on he
single cell cap u e me hod: low cy ome y [e.g., Sma -Seq2,
(Picelli e al., 2013)], mic o luidics [e.g., C1 CAGE (Kouno e al.,
2019)], d ople s [e.g., 10x Ch omium, D oNc-seq (Habib e al.,
2017)], D op-Seq (Macosko e al., 2015), mic owell [e.g., Seq
Well (Gie ahn e al., 2017)], and indexing me hods [e.g., sciRNA-
Seq (Cao e al., 2017)]. Flow cy ome y and mic o luidics-based
me hods enable us o ob ain addi ional biologically ele an
da a o he han gene le el exp ession. Fo example, Sma -
seq de ec s iso o m-le el exp ession and mu a ions in exon
egions, and C1 CAGE quan i ies e en non-polyadenyla ed RNA
such as enhance RNAs. Mul iomics analysis echniques ha e
been de eloped o low cy ome y-based me hods. Fo example,
scDam&T-seq can analyze RNA le el and p o ein binding si es
simul aneously (Rooije s e al., 2019). Howe e , hese me hods
can only measu e a maximum amoun o a ew hund ed cells pe
expe imen . As such, hey a e no sui able o cha ac e iza ion
o a e cell popula ions, as a la ge numbe o cells needs o be
analyzed on a single cell le el in hese cases. D ople s, mic owell
and indexing me hods sequence only pa s o RNA molecules (in
mos cases only he 3’ end o he RNA) e e sed ansc ibed by
oligo dT. Thus hese me hods a e unable o measu e iso o m-
le el exp ession and non-poly(A)-con aining RNAs. Howe e ,
hese me hods can measu e o e 1000 cells pe expe imen and,
as such, a e sui able o de ec a e cell popula ions.
LIMITED MEASUREMENT OF PROTEIN
EXPRESSION
A ew ecen scRNA-Seq echniques a e able o measu e p o ein
exp ession by using oligo conjuga ed an ibodies. Fo example,
CITE-Seq uses oligo-conjuga ed an ibodies agains cell su ace
ma ke s o quan i y p o ein exp ession le els (S oeckius e al.,
2017). Howe e , cu en ly, CITE-seq allows he measu emen
o a limi ed numbe o p o eins on he cell su ace. Measu ing
exp ession o in acellula p o eins by sequencing-based me hods
emains challenging.
NOISE LEVEL
Compa ed o bulk RNA-Seq, scRNA-seq da a is in insically noisy
and highly spa se especially due o so-called d opou e en s -
cases in which genes a e no de ec ed despi e being exp essed.
Technical a iabili y accoun s o app oxima ely 50% o cell-cell
a ia ion in exp ession measu emen s and a ec s downs eam
analyses such as clus e ing and pseudo ime econs uc ion. In
ac , a la ge ac ion o s ochas ic allele-speci ic exp ession can
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FIGURE 1 | Po en ial di e en ial spa ial dis ibu ion and clus e ing beha io o RNA ansc ip s in cells, which sha e he same iden i y based on single-cell RNA
sequencing. (A) Single-cell RNA-seq wo k low, which ypically yields -SNE plo s shown in (B). B Cells ha a e classi ied as belonging o he same sub ype/g oup
based on RNA ansc ip coun migh di e in he subcellula localiza ion o a ious RNA ansc ip s. (C) Spa ially con ined RNA ansc ip s may exhibi non-clus e ed
spa ial dis ibu ion o localize in clus e s. (D) Spa ial subcellula localiza ion o RNA ansc ip s may be co ela ed wi h o independen o speci ic cellula s uc u es,
o ganelles o ma ke s.
be explained by echnical noise, especially o genes exp essed
a low and mode a e le els (Kim e al., 2015). Al hough spike-
in con ols - syn he ic nucleic acids used o e o calib a ion
ha aid in co ec ing noise, can be used (Lun e al., 2017), hese
con ols a e no applicable o d ople -based echniques. Se e al
me hods ha e been de eloped o denoise da a by compu a ionally
impu ing missing alues, including MAGIC, SAVER, scImpu e,
DeepImpu e and o he s (Huang M. e al., 2018;Li and Li,
2018; an Dijk e al., 2018;A isdakessian e al., 2019;Luecken
and Theis, 2019). S ill, i emains challenging o dis inguish
echnical d opou e en s om biological e en s. Addi ionally,
i is impo an o eg ess ou biological e en s ha a e no o
in e es . Fo example, a no el T cell popula ion was iden i ied
only a e emo al o gene exp ession go e ned by he cell cycle
(Bue ne e al., 2015).
LACK OF SPATIAL AND TEMPORAL
INFORMATION
S anda d scRNA-Seq me hods canno accoun o spa ial o
empo al in o ma ion, wi h some o he main limi a ions
being he equisi e cell-dissocia ion s ep, which dis up s he
mic oen i onmen and des oys all spa ial ela ionships be ween
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cells, and he cell-lysis s ep, du ing which spa ial in o ma ion
a he subcellula le el is los . Addi ionally, his la e s ep
induces a i ac s and migh dis o cell ype iden i ica ion
(Adam e al., 2017). Recen a emp s o add ess spa ial and
empo al aspec s by RNA-seq ha e eme ged. The i s a emp
a p ese ing spa ial in o ma ion in single cell RNA-Seq used
in si u ampli ica ion by padlock p obe and RNA sequencing
by liga ion (Ke e al., 2013). In a me hod dubbed FISSEQ, Lee
e al. (2015) con e ed RNA in ixed cells and issues in o c oss-
linked cDNA amplicons, ollowed by manual sequencing on a
con ocal mic oscope. This allowed o en ichmen o con ex -
speci ic ansc ip s, while p ese ing issue and cell a chi ec u e.
While in si u RNA-Seq echniques p o ide he exp ession da a
o highly mul iplexed genes wi h high spa ial esolu ion, analysis
o he whole ansc ip ome emains challenging. On he o he
hand, non-in si u spa ial sequencing echniques ha e been
de eloped. “Spa ial ansc ip omics” (ST) (S åhl e al., 2016)
and high densi y spa ial ansc ip omics (HDST) (Vicko ic
e al., 2019) make use o a slide p in ed wi h an a ay o
e e se ansc ip ion oilgo(dT) p ime s, o e which a issue
sample is laid. This allows o imaging, ollowed by in si u
un a ge ed cDNA syn hesis and RNA-seq. Read coun s can be
co ela ed back o he mic oa ay spo and loca ion wi hin he
sample. This has a 2D spa ial esolu ion o ∼100 and 2 µm
(o se e al cells, and less han 1 cell) pe spo in ST and
HDST, espec i ely. The ST echnique is now comme cialized as
Visium om 10X genomics. Rod iques e al. (2019) sough o
add ess he ques ion o cell-scale spa ial esolu ion in a issue
by de eloping SlideSeq. This me hod unc ions by ans e ing
RNA om issue sec ions on o a su ace co e ed in DNA-
ba coded beads wi h known posi ions. The posi ional sou ce o
he RNA wi hin he issue can hen be deduced by sequencing.
In addi ion o a ay-based app oaches, a ew pionee ing me hods
ha e been de eloped o ob ain spa ial in o ma ion a cell-cell
in e ac ions by compu a ional in e ence, physical sepa a ion by
lase mic odissec ion and gen le issue dissocia ion (Sa ija e al.,
2015;Moo e al., 2018;Giladi e al., 2020). By combining in si u
hyb idiza ion images, Sa ija e al. in e ed cellula localiza ion
compu a ionally. Al hough his app oach is widely applicable,
i is challenging o apply o issues whe e he spa ial pa e n
is no ep oducible, such as in a umo , o issues whe e cells
wi h highly simila exp ession pa e ns a e spa ially sca e ed
ac oss he issue. While mic odissec ion app oaches achie e
highe spa ial esolu ion compa ed o a ay-based echniques
such as Slide-Seq, hese app oaches only wo k when he sou ce
o spa ial a iabili y has a cha ac e is ic mo phological co ela e.
Giladi e al. (2020) in oduces a new me hod, PIC-seq, which
combines cell so ing o physically in e ac ing cells (PICs) wi h
single-cell RNA sequencing and compu a ional modeling o
cha ac e ize cell-cell in e ac ions and hei impac on gene
exp ession. This app oach has a ew limi a ions: double s migh
cause mis-iden i ica ion o cell-cell in e ac ion, and i is no
sui able o use on in e ac ing cells ha ha e simila exp ession
p o iles. While hese non-in si u echniques can achie e highe
de ec ion sensi i i y han in si u RNA-Seq a single-cell o nea ly
single-cell esolu ion, we sugges ha u he p ecise spa ial
in o ma ion o RNAs and p o eins in he cell is equi ed o ully
unde s and cell s a e, as exempli ied by P g anules (see sec ion
“Discussion” below).
To unde s and he ansi ion be ween cell s a es and
di e en ia ion s ages, empo al analyses o he ansc ip ome
and epigenome a e essen ial. The majo i y o sequencing-
based app oaches p o ide only a “snapsho ” pe spec i e o any
sample, and do no allow us o place he in o ma ion in he
empo al con ex . To add ess his limi a ion, o e 70 me hods
o econs uc pseudo ime ha e been de eloped (Re iewed
in Saelens e al., 2019;G ün and G ün, 2020), allowing o
he cha ac e iza ion o biological p ocesses’ dynamics mo e
accu a ely han con en ional ime se ies o bulk RNA-Seq
(T apnell e al., 2014;Ji and Ji, 2016;Reid and We nisch, 2016;
Qiu e al., 2017;Chen Y. e al., 2019). Fo example, Monocle
(T apnell e al., 2014), uses single-cell RNA-seq da a collec ed
a mul iple ime poin s o cha ac e ize he empo al aspec
o gene exp ession. This was used o cha ac e ize di e ences
in gene exp ession in di e en ia ion o p ima y human
myoblas s (T apnell e al., 2014). TSCAN uses RNA-seq da a o
compu a ionally o de cells in a he e ogenous popula ion based
on he g adual ansi ion o hei gene exp ession (Ji and Ji, 2016).
Addi ionally, SPRING is able o isualize long con inuous gene
exp ession opologies, ep esen ing a powe ul ool o isualize
complex di e en ia ion p ocesses such as b anching opology o
hema opoie ic p ogeni o cells and hei di e en ia ion (Wein eb
e al., 2018). Ano he ecen ly de eloped me hod, RNA eloci y,
econs uc s ajec o y based on kine ics o nascen and ma u e
mRNA o mo e solid quan i a i e ounda ion (La Manno
e al., 2018). These pseudo ime econs uc ion app oaches can
aid in un eiling bo h ansi ions in gene exp ession and he
dynamics o ansc ip ional egula ion o cha ac e iza ion o
gene egula o y ne wo ks.
The elucida ion o gene egula o y ne wo ks can enhance
ou unde s anding o complex cellula p ocesses in li ing cells
as a b idge connec ing geno ypes and pheno ypes. T adi ional
app oaches o ansc ip ome p o iling ha e been success ully
used o in e and cha ac e ize egula o y ne wo ks o e ime
cou ses such as in di e en ia ion. A no able example is
FANTOM5 phase2, which e ealed gene egula o y ne wo ks
by dense ime cou se analyses wi h CAGE (A ne e al., 2015;
Baillie e al., 2017). Ne wo k cons uc ion om econs uc ed
pseudo ime in scRNA-Seq is challenging due o a combina ion
o biological a ia ion (e.g., s ochas ici y o bu s s) and echnical
limi a ions, such as he inabili y o cap u e non-poly(A) RNAs.
To da e, he e a e only small-scale e o s o de i e egula o y
ne wo ks om single-cell ansc ip omics da a o e ime cou ses.
In p inciple, pseudo ime econs uc ed om scRNAseq da a
allows in e ence o gene- egula o y ne wo ks (Aiba e al.,
2017). A ecen s udy showed ha combining me hods o
ne wo k econs uc ion wi h RNA eloci y imp o es he accu acy
o ne wo k in e ence, hus imp o ing he empo al coupling
measu emen o mo e accu a e econs uc ion (Qiu e al., 2020).
Howe e , as pseudo ime econs uc ion me hods a e based on
he assump ion ha changes in gene exp ession a e con inuous
o g adual, he app oach is no able o cap u e ei he d as ic
o ansien changes in ansc ip ion ha may occu du ing a
p ocess o in e es . In addi ion, we ha e o keep in mind ha
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scRNA-Seq me hods, especially d ople based echniques, migh
cap u e double s, which unless iden i ied and emo ed, migh be
mis aken as ansien cell s a es (Kisele e al., 2019). As wi h
con en ional RNA-seq, hese app oaches a e also unable o ake
in o conside a ion he spa ial dis ibu ion o RNAs, despi e he
impo ance o changes in RNA subcellula dis ibu ion du ing
p ocesses such as de elopmen and di e en ia ion.
SPATIAL AND TEMPORAL
INFORMATION CAN INFORM A MORE
IN-DEPTH SUB-CLASSIFICATION OF
CELLS
Al hough he e is apid and ongoing de elopmen o
sequencing-based echnologies, hei capabili ies o de e mine
high- esolu ion spa ial and empo al in o ma ion a e limi ed.
Eme ging e idence indica es ha , a a gi en poin in ime,
no only RNA and p o ein abundance (Co e e al., 2016), bu
also di e en ial subcellula dis ibu ion o hese molecules
con ibu es o a cell’s s a e and unc ion ( o example: Moo e al.,
2017). Conside once mo e ou hypo he ical si ua ion: can we
conclude ha wo cells, adjacen o one ano he in a issue, which
possess simila le els o he same RNA ansc ip s, a e he same
cell ype (Figu e 1B). By inc easing he spa ial esolu ion a which
we assess hese samples, we may obse e ha al hough a simila
concen a ions, a pa icula RNA species could be di e en ially
localized in hese cells. Fo ins ance, he RNA may be dispe sed
ac oss he cy oplasm in one cell and locally clus e ed in he
o he (Figu es 1B,C). The subcellula dis ibu ion o mRNA
ansc ip s can de e mine hei binding pa ne s and in luence
hei a e o ansla ion, a ec ing he cellula concen a ion and
localiza ion o he p o ein p oduc (Ka z e al., 2012, 2016;Moo
e al., 2018). This, in u n, can in luence he cell’s unc ion and
capaci y o espond o a ious en i onmen al cues. Addi ionally,
we may obse e spa ial posi ioning o ce ain ansc ip s in close
associa ion wi h subcellula landma ks o o ganelles (Sa ulescu
e al., 2019, e iewed in Su e , 2018;Hughes and Simmonds, 2019
and o he s) (Figu e 1D), including memb aneless o ganelles
such as s ess g anules ( o example Khong e al., 2017;Pad ón
e al., 2019;Wilbe z e al., 2019). This could indica e a unc ional
ela ionship be ween he RNA’s cellula ole and b oade cellula
p ocesses, such as cell di ision, di e en ia ion, pola iza ion
e c.’ Fu he , his may also in luence, o be in luenced by, cell
s a e o iden i y.
The signi icance o RNA/p o ein subcellula dis ibu ion o e
bo h space and ime can be illus a ed using he example o P
g anules in C. elegans de elopmen (B angwynne e al., 2009).
Upon pola iza ion o a C. elegans single-cell emb yo along
he an e io -pos e io axis, hese RNA- and p o ein-con aining
condensa es shi om a uni o m dis ibu ion o localize a he
pos e io hal o he cell. This di e en ial dis ibu ion de e mines
he ge m line and soma ic cell a es o he di iding emb yo’s
daugh e cells. Thus, al hough he unc ion o P g anules is no
ully unde s ood ye , he spa ial dis ibu ion o hese g anules
ep esen s a cell-s a e ansi ion ma ke ( he eadiness o he
cell o p og ess o he wo-cell s age) as well as a ma ke
o de e mina ion o he cell a e (p ogeni o ge m cell and
soma ic sis e cell). Impo an ly, applica ion o cu en single-
cell sequencing and pseudo ime econs uc ion me hods in his
sys em would no ully e eal he ansi ion o cell s a e o ype,
as changes in e ed by RNA and p o ein spa ial localiza ion
in P g anules and possibly addi ional s uc u es would no
be de ec ed. Simila p ocesses in ol ing localiza ion o mRNA
ansc ip s occu in o he de elopmen al sys ems, including
de e mina ion o spa ial pa e ning in he de eloping D osophila
emb yo (Johns one and Lasko, 2001) and de e mina ion o cell
a e in he Xenopus oocy e (King e al., 2005). Such sys ems
highligh a need o echnologies capable o accoun ing o bo h
he empo al and spa ial aspec s o single-cell genomics o
unde s anding cell ypes and s a es.
In a manne simila o mRNA, o he species o RNA may be
subjec o such spa ial o ganiza ion. Ou unde s anding o long
non-coding RNA (lncRNA) unc ion and beha io is s ill in i s
in ancy. Howe e , he in e sec ion o he subcellula o ganiza ion
and unc ion o long non-coding RNAs may con ibu e o a ine
classi ica ion o cellula iden i ies and p o e o be a pa icula ly
in e es ing ield o disco e y in he u u e. O e all, subcellula
RNA dis ibu ion, as well as in e ac ions be ween ansc ip s
and cellula s uc u es o a icking and packaging, may di e
be ween o he wise simila cells. I hese di e ences lead o
unc ional dis inc ions be ween he cells, can we s ill conside
hem o ha e he same iden i y? Expanding on his pe spec i e,
po en ially hund eds o RNA ansc ip s in a gi en expe imen
may be p esen a he same le el be ween cells; howe e
hese ansc ip s migh be di e en ially dis ibu ed wi hin hem.
Thus a ma ix o housands o po en ial combina ions o RNA
localiza ion pa e ns may exis , sugges ing he possibili y o a
la ge a ay o g anula ly di e en ia ed cell sub ypes ha migh
ha e p e iously been classi ied as belonging o he same g oup.
While he discussion he e has ocused on cell classi ica ion
by ansc ip ional da a, i is impo an o ecognize ha
p o eomic da a and he spa ial o ganiza ion o a cell’s p o ein
epe oi e could po en ially con ibu e o cell classi ica ion
decisions. Recen la ge scale s udies indica e ha cells ha appea
gene ically iden ical display a ious p o ein le els and subcellula
localiza ions o p o eins (Sigal e al., 2006;B eke e al., 2013;Thul
e al., 2017;Lu e al., 2018) du ing di e en ia ion (Balázsi e al.,
2011;Rubakhin e al., 2013), in esponse o en i onmen al s imuli
(Na ayanaswamy e al., 2009;Balázsi e al., 2011;B eke e al.,
2013) o d ug ea men (Tkach e al., 2012;Déne aud e al.,
2013;To es e al., 2016;I zhak e al., 2017;Sha e e al., 2017).
This could be due o local ansla ion o di e en ially dis ibu ed
mRNAs, o pos - ansla ional modi ica ions o he p o ein,
in e ing di e en ial in e ac ions wi h binding pa ne s. Simila ly
o RNA, his phenomena may apply o mul iple di e en p o ein
species. In eg a ed wi h he spa ial dis ibu ion in o ma ion o
RNAs, his could exponen ially expand he ma ix o cellula
o ganiza ions, highligh ing he po en ial o in-dep h cellula
classi ica ion o accu a ely esol ing cell s a es and iden i ies.
The classic app oach o s udy spa ial p o eomics elies
on subcellula ac iona ion o o ganelles coupled wi h mass
spec ome y analysis. Addi ional spa ial in o ma ion a
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high esolu ion can be ob ained om app oaches combining
p oximi y labeling media ed by enginee ed asco bic acid
pe oxidase (APEX) o an ibody-media ed a ini y pu i ica ion
wi h mass spec ome y ( o example Hein e al., 2015;Hu lin
e al., 2017;Lobingie e al., 2017;Paek e al., 2017). This allows
o he cha ac e iza ion o he in e ac ome o a p o ein, based
on he assump ion ha p o eins mus be in close p oximi y
o be able o in e ac . As such, i is also indica i e o he local
spa ial p o eome. Howe e , hese app oaches a e s ill in hei
in ancy, and in-dep h co e age o he cell p o eome has no
been comple ed o da e (Re iewed in Lundbe g and Bo ne ,
2019). A complemen a y app oach o mass spec ome y-based
me hods elies on imaging o p o eins on a p o eome-wide
le el and a single-cell esolu ion (Re iewed in Lundbe g and
Bo ne , 2019). The Human P o ein A las (HPA) ini ia i e
aims o map he spa ial subcellula dis ibu ion o all human
p o eins in all cell ypes o he human body (Thul e al., 2017;
Uhlen e al., 2010;Uhlén e al., 2015;Uhlen e al., 2017). The
spa ial dis ibu ion o an ex ensi e numbe o p o eins has
been de e mined, using an ibody labeling, con ocal mic oscopy,
and manual and compu a ional image analysis, allowing he
de ailed classi ica ion o subcellula localiza ion o hese p o eins.
Ongoing esea ch is being conduc ed o u he cha ac e ize
he spa ial dis ibu ion o p o eins, making use o echnological
ad ances in mul iplexed imaging, endogenous p o ein agging,
au oma ed luo escence mic oscopy and image analysis ools,
including deep neu al ne wo ks (Re iewed in Lundbe g and
Bo ne , 2019). I can easonably be expec ed ha simila
la ge-scale app oaches could be applied o he s udy o RNA
subcellula localiza ion, and allow he cha ac e iza ion o he
spa ial dis ibu ion o po en ially all cellula RNAs in cell lines
and issues. This would aid in he ine g ained subclassi ica ion
o cell ypes and s a es.
ANALYTICAL AND IMAGING-BASED
METHODS REQUIRED TO ANALYZE
SPATIAL INFORMATION
Gi en he complexi y o subcellula o ganiza ion, and a
cell’s inhe en s a e o lux, we an icipa e ha in-dep h
cha ac e iza ion o he subcellula o ganiza ion o mul iple
molecula species ac oss la ge numbe s o cells will equi e
ad ances in analy ical imaging-based me hods. Such me hods
would need (1) he capaci y o label mul iple RNA ansc ip s
and p o eins in a mul iplexed manne ; (2) acquisi ion o
da a a bo h high spa ial esolu ion and high h oughpu ;
and (3) compu a ional amewo ks o quan i a i e image
analysis o la ge, mul i-dimensional imaging da a se s. The
la e would be pa icula ly impo an o dis inguish be ween
he sub le di e ences in spa ial dis ibu ion o molecules
which could occu be ween cells. Fu he , hese ools need
o be adap able be ween cul u ed cell monolaye s, la ge
3D cul u es, including sphe oids and o ganoids, and in ac
issue samples. This would ensu e he cap u e o spa ial
in o ma ion unde con olled condi ions and om cells in hei
na i e issue con ex .
Se e al labeling and imaging modali ies ha e been de eloped
o mee condi ions (1) and (2). E o s o inc ease he labeling
sensi i i y and h oughpu capaci y o hyb idiza ion-based
echniques ha e led o he eme gence o se e al sophis ica ed
RNA FISH ( luo escen in si u hyb idiza ion) echniques, in some
cases pai ed wi h a ge ed in si u cDNA syn hesis and sequencing.
Con en ional single molecule FISH (smFISH) makes use o
mul iple sho single-s anded DNA oligonucleo ide p obes, each
labeled wi h a single luo opho e, o a ge and speci ically label
mRNA (Raj e al., 2008). A key adap a ion o inc eased RNA
FISH labeling capaci y has been he use o sequen ial ounds
o mul i-colo labeling and imaging o he same sample. An
in ui i e a ia ion o his app oach is massi ely mul iplexed cyclic
smFISH, such as osmFISH (Codeluppi e al., 2018), which was
used o label 33 a ge ed gene ansc ip s o e 13 ounds o
labeling o map he cellula a chi ec u e o he mouse neu al
co ex. Labeling capaci y is u he boos ed by he adop ion
o FISH p obe ba coding app oaches, along wi h sequen ial
labeling, as demons a ed wi h mul iplexed e o - obus FISH
(MERFISH) (Chen e al., 2015;Mo i and Zhuang, 2016)
and sequen ial FISH (seqFISH and seqFISH +) (Lubeck e al.,
2014;Eng e al., 2019). These echniques begin o app oach
ull- ansc ip ome imaging, wi h he capaci y o label 100 o
10000 s o RNA species a single cell, subcellula (Lubeck e al.,
2014;Chen e al., 2015) o e en sub-di ac ion (Eng e al.,
2019) esolu ion. STARmap (Wang e al., 2018) uses in si u
ampli ica ion o a ge -speci ic p obe ba code egions, ha can
be decoded by 3D sequencing wi hin samples con e ed o a
hyd ogel ma ix. This allows o he de ec ion o 1000 s o
RNA species in la ge cell numbe s in 3D issue s uc u es.
While hese app oaches can all p o ide g ea insigh in o
he unc ional cellula o ganiza ion wi hin issues, hey each
ha e a ying limi a ions in he spa io empo al esolu ion o
h oughpu a ailable.
Many o hese app oaches bene i om specialized LabWa e
and equipmen . The e is an inc easing ease o access o a o dable
liquid handling sys ems, d i en by open we -lab solu ions such
as OpenLH (Gome e al., 2019) and modula Lego-based and
3D-p in ed injec ion pumps ( o example Almada e al., 2019).
Such sys ems a e essen ial o high-cycle sequen ial labeling o
ens o hund eds o molecula a ge s, which can be labeled in a
single sample wi hou ba coding, as demons a ed wi h osmFISH
(Codeluppi e al., 2018). Au oma ed mic oscopes allow inc eased
imaging h oughpu o bo h sample size and numbe , howe e
his is ypically done a low magni ica ion and esolu ion.
High- esolu ion isualiza ion o molecula a ge s is usually
limi ed o he single-cell scale. A new imaging modali y e med
syn he ic ape u e op ics (i.e., S ella ision Mic oscope, Op ical
Biosys ems) (desc ibed in Ryu e al., 2006) uses in e e ome y
o inc ease he e ec i e esolu ion o low magni ica ion imaging.
This d i es signi ican ly highe h oughpu (∼100 s–1000 s
o cells) o high esolu ion (subcellula and up o single-
molecule) imaging. Such a sys em, pa icula ly i coupled
wi h on-line luid handling o sequen ial labeling, would
be well sui ed o quan i a i e spa ial cha ac e iza ion o he
molecula epe oi e o la ge numbe s o indi idual cells o
issue sec ions.
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Requi emen (3), he a ailabili y o compu a ional amewo ks
o quan i a i e image analysis o la ge imaging da a se s
is an a ea o apid ongoing de elopmen . This spans he
ull pos acquisi ion pipeline, om basic image p ocessing
o spo de ec ion, decoding, quan i ica ion, and classi ica ion.
Quan i a i e image analysis ools commonly used o image-
based pheno ypic cell p o iling include ways o acili a e
ea u e ex ac ion, da a quali y con ol and no maliza ion,
dimensionali y educ ion and clus e ing om la ge numbe s o
cells (desc ibed in Caicedo e al., 2017). Many o hese echniques
could be adap ed o he analysis o spa ial and empo al
cha ac e iza ion by ansc ip omics. Wi h he ma u a ion o deep
lea ning echnology, he e is also ex ensi e po en ial o he
applica ion o a i icial neu al ne wo k-based app oaches o
image p ocessing and analysis. Deep lea ning-based app oaches
a e ypically bes -sui ed o he p ocessing o la ge se s o
complex da a wi h many pa ame e s, as would be expec ed o
hese imaging assays.
Applica ions o neu al ne wo k-based app oaches include
image denoising, segmen a ion and ca ego iza ion. Se e al neu al
ne wo k-based ools a e al eady a ailable o he es o a ion o
images wi h high noise le els ( o example Weige e al., 2018;
Ba son and Roye , 2019;K ull e al., 2019). Tools o ne wo k-
based FISH spo de ec ion ha e likewise s a ed o eme ge ( o
example Gudla e al., 2017;Mabaso e al., 2018). A common
challenge wi h smFISH applica ions is he densi y o signal,
pa icula ly in he case o abundan ansc ip s. This is linked
o high le els o backg ound signal, comp omising he signal-
o-noise a io (SNR) and ou abili y o au oma ically de ec and
quan i y spo s. Al eady-a ailable ne wo k-based denoising and
spo de ec ion ools could be u he adap ed o he pa icula ly
challenging low SNR and high haze condi ions commonly
encoun e ed in smFISH. In addi ion o spo de ec ion, spa ially
esol ed ansc ip omics necessi a es he abili y o dis inguish
indi idual and adjacen cells om each o he , and a way o
cha ac e ize he dis ibu ion o FISH spo s ac oss indi idual
cells. While he e a e a la ge numbe o cell segmen a ion
ools a ailable ( e iewed in Meije ing, 2012 and Vica e al.,
2019), he au oma ed segmen a ion o densely packed cells and
nuclei, ei he in a cul u ed monolaye o in ac issue sec ions
emains a challenge. Po en ial solu ions o his may also lie in
machine lea ning and ne wo k-based app oaches ( o example
Al-Ko ahi e al., 2018;Schmid e al., 2018;Be g e al., 2019).
Beyond image p ocessing, cha ac e iza ion o cellula FISH
spo dis ibu ion pa e ns, including quan i ica ion pe cellula
compa men , could make use o simila app oaches o hose
used o localiza ion pa e n classi ica ion in spa ial p o eomics.
These include K-nea es neighbo classi ie s, suppo ec o
machines, a i icial neu al ne wo ks and decision ees ( e iewed
in Lundbe g and Bo ne , 2019).
As expe imen al echnologies de elop and gene a e high-
esolu ion spa ial and empo al cell cha ac e iza ion da ase s,
ongoing de elopmen o ools and pla o ms o analyze his
da a will be impe a i e. Many o he no el image p ocessing
and analysis ools desc ibed abo e equi e op imiza ion o high
h oughpu . In addi ion, he e is a need o de elopmen o
comple e analy ical pipelines and amewo ks o p ocessing and
ex ac ing he complex in o ma ion and pa e ns om hese
imaging da a se s. Ea ly i e a ions o such amewo ks can be seen
in eme ging pla o ms such as DypFISH (Dynamic pa e ned
FISH) (Sa ulescu e al., 2019), and S a ish, unde de elopmen
by he SpaceTx conso ium in associa ion wi h he Human Cell
A las p ojec (desc ibed in Pe kel, 2019). DypFISH is a ecen ly
de eloped analy ical pla o m o quan i a i e cha ac e iza ion
o he spa ial and empo al subcellula dis ibu ion o key
biomolecules a a single cell le el. This sys em makes use o
mic opa e ning o cons ain he a chi ec u e o he cell, in e ing
a educ ion in a ia ion o subcellula dis ibu ion o mRNA and
p o ein and allowing o high ep oducibili y. This app oach was
used o quan i y he co ela ion o mRNA and p o ein spa ial
dis ibu ions and he MTOC (a key indica o o a cell’s pola i y)
in mouse ib oblas s, e ealing impo an spa ial and empo al
di e ences be ween mRNA species, as well as wi hin an mRNA
species du ing pola iza ion. This may indica e di e en ial cell
s a e-dependen spa ial dis ibu ion o impo an biomolecules.
DypFISH may hus be a i s s ep in es ablishing a mo e
comp ehensi e app oach o he cha ac e iza ion o spa ial and
empo al in o ma ion in issues and o he biological sys ems wi h
high le els o complexi y. The S a ish pla o m seeks o add ess
c i ical aspec s o da a handling and p e-p ocessing, as well as
spo de ec ion and RNA iden i ica ion in a lexible manne . This
enables he pla o m o handle da a se s om mul iple echniques
al eady desc ibed he e, and ex ac and compa e in o ma ion
ac oss di e en expe imen s. Mo e ecen ly, Spa ialDB (Fan e al.,
2020) has been se up as a manually cu a ed and explo able
eposi o y o spa ially esol ed ansc ip omic da ase s om
mul iple echniques. As hese analy ical pla o ms de elop, we
expec ha he in eg a ion o each o hese ools in o a single
amewo k, in a modula manne , will be bene icial o esea che s
seeking o unde s and cell iden i y and di e ences in biology.
Indeed, he e is al eady a pa allel d i e o he in eg a ion
o single cell sequencing app oaches wi h imaging-based
app oaches. RNAscope, which makes use o b anched DNA,
(Wang e al., 2012, ma ke ed by ACDBio) has been shown o
be amenable o mul iplexing and image-based ansc ip omics,
especially when pai ed wi h app oaches such as au oma ed
liquid handling (Ba ich e al., 2013) and FISH p obe ba coding
(Xia e al., 2019). Mo e ecen ly, RNAscope has been pai ed
wi h scRNA seq o demons a e molecula he e ogenei y
and cellula dynamics in epide mal wound healing (Haensel
e al., 2020). scRNA seq, combined wi h bo h ST and
a ge ed in si u sequencing, has been used o compile an
a las o he de eloping human hea (Asp e al., 2019). In
ano he case, ST has been combined wi h scRNA seq o
po ions o he same issue sample, o cha ac e ize he issue
a chi ec u e in panc ea ic duc al adenoca cinoma (Moncada
e al., 2020). Compu a ional app oaches o allow he in eg a ion
o hese dis inc ypes o da a a e also apidly de eloping.
Moncada e al. (2020) made use o mul imodal in e sec ion
analysis o in eg a e he image and sequencing da a. O he
ecen ly ad anced analy ical me hods include he use o
p obabilis ic models (Ande sson e al., 2019), supe ised lea ning
app oaches o mixed-da a decomposi ion (Cable e al., 2020),
and SPOTligh (Elosua e al., 2020), which uses non-nega i e
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ma ix ac o iza ion eg ession models o he decon olu ion
o ST spo da a.
DISCUSSION
The in ica e ela ionship be ween a cell’s subcellula molecula
o ganiza ion, i s spa io empo al con ex wi hin a issue and
sys em, and i s iden i y and unc ion has a signi ican impac
on ou unde s anding o cell biology. The bene i o high-
esolu ion spa io empo al cell ype cha ac e iza ion, aking in o
accoun issue- o subcellula - scale in o ma ion, is e iden o a
numbe o esea ch ields, including de elopmen al biology (Asp
e al., 2019), cance esea ch ( o example Baccin e al., 2020;
Moncada e al., 2020 and Yoousu e al., 2020), and p ecision
medicine (Pe i p ez e al., 2018). Spa io empo al cha ac e iza ion
o he umo mic oen i onmen , o example, can p o ide insigh
in o he composi ion, o ganiza ion and unc ionali y o umo -
associa ed cells, and hei oles in umo de elopmen and
se e i y o disease ( e iewed in Pe i p ez e al., 2018). This
kind o esea ch also holds he po en ial o mo e sophis ica ed
app oaches o ea ing malignan umo s, o example, whe e
cells may ha e p e iously unde app ecia ed ansi ional s a es
ha can be a ge ed. I may also be in e es ing o assess i
di e en ial spa ial and empo al dis ibu ion o key disease
bioma ke s, in addi ion o hei exp ession le els, could be
linked o a ia ion in esponse o ea men be ween pa ien s.
Recen s udies a single-cell esolu ion indica e ha subcellula
spa io empo al ansc ip omic cha ac e iza ion could also help
us unde s and he molecula basis and p og ession o ce ain
gene ic diso de s, such as A hy hmogenic ca diomyopa hy
(Booge d e al., 2019) and cogni i e diseases such as Alzheime ’s
Disease (Chen W.-T. e al., 2019) and Pa kinson’s Disease
(Aguila e al., 2019).
The ole o cell s a e is also inc easingly app ecia ed in
in ec ion and immuni y ( e iewed in Kunz e al., 2018),
pa icula ly in in ec ious diseases whe e immune egula ion is
key o disease ou comes. Fo example, he lineage and me abolic
s a e o mac ophages can ha e p o ound e ec s in Mycobac e ial
ube culosis in ec ion (Huang L. e al., 2018, e iewed in Shi e al.,
2019). Full cha ac e iza ion o mac ophage cell ypes and s a es
may imp o e ou unde s anding o , and abili y o be e ea ,
TB disease. T ansc ip ion-based cell classi ica ion is inhe en
o ini ia i es such as he Human Cell A las p ojec . He e, oo,
he inco po a ion o high- esolu ion spa io empo al in o ma ion
holds impo an po en ial o u he biological insigh s and may
g ea ly enhance he ansla ional bene i s o hese ini ia i es.
Cu en cell classi ica ion p ocesses will need o be adap ed o
include his highe g anula i y o in o ma ion, and hese la ge-
scale p ojec s can be expec ed o d i e he in eg a ion o no el
expe imen al and imaging echnologies o spa io empo ally
esol ed cha ac e iza ion. This will include new and ad anced
analy ical app oaches and da a ep esen a ion me hods. Such
de elopmen s can highligh and make accessible he weal h o
in o ma ion a ailable by hese app oaches.
Th oughou his Pe spec i e we ha e emphasized he
need o ake in o accoun spa io empo al in o ma ion when
cha ac e izing cell s a e and iden i y. As we ha e discussed,
an inc easing body o da a suppo s he e ec o a ia ion
in mRNA/p o ein exp ession and subcellula localiza ion in
di ec ing cell s a e and iden i y. Ne e heless, i is impo an o
acknowledge ha no all such a ia ion is necessa ily associa ed
wi h unc ional changes in cell s a e. T ansc ip ional egula ion,
a a single-cell le el in mammalian cells, is p obabilis ic and
in e mi en . This leads o p oduc ion o mRNA ansc ip s in
pulses and can con ibu e o cell- o-cell he e ogenei y (Femino
e al., 1998;Cos elloe e al., 1999, e iewed in Hume, 2000).
This, again, poin s o he ques ion o how we delinea e cell
s a e and iden i y, using in eg a ed single-cell ansc ip omics
and issue-le el and subcellula spa ial o ganiza ion da a. Mul i-
scale spa io empo al in o ma ion, o e a la ge numbe o cells
and samples, is needed o us o quan i a i ely assess he ex en
o a ia ion ac oss issues and wi hin cells, and o de ec a e
e en s and cell ypes. Assessmen o such da a should ake
place in he con ex o ou inc easing unde s anding o basic
in acellula p ocesses, and he unc ions o issues and disease
s a es being s udied. These s udies and analyses will be key in
ueling impo an discussions wi hin he ield. Pe inen ly, he
di e en scales o esolu ion ha may apply o di e en ques ions
should be ca e ully conside ed. Single-cell ansc ip omics and
subcellula spa io empo al o ganiza ion con ibu e o cell s a e
and iden i y, and can con ibu e o issue unc ion. Howe e ,
his subcellula esolu ion may no always be necessa y o
unde s and he ole o indi idual cells and how hey in e ac
wi h neighbo ing cells in he con ex o hei issue. A ela ed
discussion is necessa y a ound wha “ h eshold” (o mul iple
si ua ion-dependen h esholds) o he ex en o spa io empo al
a ia ion (de ec ion o which is esolu ion-dependen ) may be
conside ed o delinea e unc ionally dis inc s a es o iden i ies
o indi idual cells.
Accoun ing o he dynamic s a es and unc ional plas ici y
a ailable o cells has al eady eme ged as impo an o he
classi ica ion and cha ac e iza ion o cell ypes. This will become
mo e widely acknowledged wi h he pa allel de elopmen
o powe ul ools and echnology o p oduce, p ocess and
mine he eme ging in o ma ion. These and o he necessa y
de elopmen s desc ibed he e will allow accu a e, high- esolu ion
cell classi ica ion and imp o ed unde s anding o he unc ion
o di e en cells in issues. Taken oge he , hese ad ancemen s
will p o ide powe ul ools o ad ances in undamen al biology,
biomedical esea ch and ela ed ields.
AUTHOR CONTRIBUTIONS
AS, CJ, LD, and MM concei ed he idea. AS, CJ, LD, and
YN w o e and p oo ead he manusc ip . All au ho s ead and
app o ed he manusc ip .
ACKNOWLEDGMENTS
We hank Timo hy de We , Dana M. Sa ulescu, Nicolas Beaume,
and Ashley J. Jacobs o ui ul discussions and c i ical eading
o he manusc ip .
F on ie s in Molecula Biosciences | www. on ie sin.o g 8Augus 2020 | Volume 7 | A icle 209
molb-07-00209 Augus 13, 2020 Time: 17:7 # 9
Sa ulescu e al. Pinpoin ing Cell Iden i y in Time and Space
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